fix: various

- Added the `LeastMeanSquareVelocityFilter` to improve tracking velocity estimation using historical 3D poses.
- Updated the `triangulate_one_point_from_multiple_views_linear` and `triangulate_points_from_multiple_views_linear` functions to enhance documentation and ensure proper handling of input parameters.
- Refined the logic in triangulation functions to ensure correct handling of homogeneous coordinates.
- Improved error handling in the `LastDifferenceVelocityFilter` to assert non-negative time deltas, enhancing robustness.

app/tracking/__init__.py

@@ -1,14 +1,19 @@
+import weakref
+from collections import deque
 from dataclasses import dataclass
-from datetime import datetime
+from datetime import datetime, timedelta
+from itertools import chain
 from typing import (
     Any,
     Callable,
     Generator,
     Optional,
+    Protocol,
     Sequence,
     TypeAlias,
     TypedDict,
     TypeVar,
+    Union,
     cast,
     overload,
 )
@@ -18,18 +23,428 @@ from beartype import beartype
 from beartype.typing import Mapping, Sequence
 from jax import Array
 from jaxtyping import Array, Float, Int, jaxtyped
-from pyrsistent import PVector
+from pyrsistent import PVector, v, PRecord, PMap
 
-from app.camera import Detection
+from app.camera import Detection, CameraID
+
+TrackingID: TypeAlias = int
+
+
+class TrackingPrediction(TypedDict):
+    velocity: Optional[Float[Array, "J 3"]]
+    keypoints: Float[Array, "J 3"]
+
+
+class GenericVelocityFilter(Protocol):
+    """
+    a filter interface for tracking velocity estimation
+    """
+
+    def predict(self, timestamp: datetime) -> TrackingPrediction:
+        """
+        predict the velocity and the keypoints location
+
+        Args:
+            timestamp: timestamp of the prediction
+
+        Returns:
+            velocity: velocity of the tracking
+            keypoints: keypoints of the tracking
+        """
+        ...  # pylint: disable=unnecessary-ellipsis
+
+    def update(self, keypoints: Float[Array, "J 3"], timestamp: datetime) -> None:
+        """
+        update the filter state with new measurements
+
+        Args:
+            keypoints: new measurements
+            timestamp: timestamp of the update
+        """
+        ...  # pylint: disable=unnecessary-ellipsis
+
+    def get(self) -> TrackingPrediction:
+        """
+        get the current state of the filter state
+
+        Returns:
+            velocity: velocity of the tracking
+            keypoints: keypoints of the tracking
+        """
+        ...  # pylint: disable=unnecessary-ellipsis
+
+
+class DummyVelocityFilter(GenericVelocityFilter):
+    """
+    a dummy velocity filter that does nothing
+    """
+
+    _keypoints_shape: tuple[int, ...]
+
+    def __init__(self, keypoints: Float[Array, "J 3"]):
+        self._keypoints_shape = keypoints.shape
+
+    def predict(self, timestamp: datetime) -> TrackingPrediction:
+        return TrackingPrediction(
+            velocity=None,
+            keypoints=jnp.zeros(self._keypoints_shape),
+        )
+
+    def update(self, keypoints: Float[Array, "J 3"], timestamp: datetime) -> None: ...
+
+    def get(self) -> TrackingPrediction:
+        return TrackingPrediction(
+            velocity=None,
+            keypoints=jnp.zeros(self._keypoints_shape),
+        )
+
+
+class LastDifferenceVelocityFilter(GenericVelocityFilter):
+    """
+    a naive velocity filter that uses the last difference of keypoints
+    """
+
+    _last_timestamp: datetime
+    _last_keypoints: Float[Array, "J 3"]
+    _last_velocity: Optional[Float[Array, "J 3"]] = None
+
+    def __init__(self, keypoints: Float[Array, "J 3"], timestamp: datetime):
+        self._last_keypoints = keypoints
+        self._last_timestamp = timestamp
+
+    def predict(self, timestamp: datetime) -> TrackingPrediction:
+        delta_t_s = (timestamp - self._last_timestamp).total_seconds()
+        assert delta_t_s >= 0, f"delta_t_s is negative: {delta_t_s}"
+        if self._last_velocity is None:
+            return TrackingPrediction(
+                velocity=None,
+                keypoints=self._last_keypoints,
+            )
+        else:
+            return TrackingPrediction(
+                velocity=self._last_velocity,
+                keypoints=self._last_keypoints + self._last_velocity * delta_t_s,
+            )
+
+    def update(self, keypoints: Float[Array, "J 3"], timestamp: datetime) -> None:
+        delta_t_s = (timestamp - self._last_timestamp).total_seconds()
+        assert delta_t_s >= 0, f"delta_t_s is negative: {delta_t_s}"
+        self._last_velocity = (keypoints - self._last_keypoints) / delta_t_s
+        self._last_keypoints = keypoints
+        self._last_timestamp = timestamp
+
+    def get(self) -> TrackingPrediction:
+        if self._last_velocity is None:
+            return TrackingPrediction(
+                velocity=None,
+                keypoints=self._last_keypoints,
+            )
+        else:
+            return TrackingPrediction(
+                velocity=self._last_velocity,
+                keypoints=self._last_keypoints,
+            )
+
+
+class LeastMeanSquareVelocityFilter(GenericVelocityFilter):
+    """
+    a velocity filter that uses the least mean square method to estimate the velocity
+    """
+
+    _historical_3d_poses: deque[Float[Array, "J 3"]]
+    _historical_timestamps: deque[datetime]
+    _velocity: Optional[Float[Array, "J 3"]] = None
+    _max_samples: int
+
+    def __init__(
+        self,
+        historical_3d_poses: Sequence[Float[Array, "J 3"]],
+        historical_timestamps: Sequence[datetime],
+        max_samples: int = 10,
+    ):
+        """
+        Args:
+            historical_3d_poses: sequence of 3D poses, at least one element is required
+            historical_timestamps: sequence of timestamps, whose length is the same as `historical_3d_poses`
+            max_samples: maximum number of samples to keep
+        """
+        assert (N := len(historical_3d_poses)) == len(
+            historical_timestamps
+        ), f"the length of `historical_3d_poses` and `historical_timestamps` must be the same; got {N} and {len(historical_timestamps)}"
+        if N < 1:
+            raise ValueError("at least one historical 3D pose is required")
+
+        temp = zip(historical_3d_poses, historical_timestamps)
+        # sorted by timestamp
+        temp_sorted = sorted(temp, key=lambda x: x[1])
+        self._historical_3d_poses = deque(
+            map(lambda x: x[0], temp_sorted), maxlen=max_samples
+        )
+        self._historical_timestamps = deque(
+            map(lambda x: x[1], temp_sorted), maxlen=max_samples
+        )
+        self._max_samples = max_samples
+        if len(self._historical_3d_poses) < 2:
+            self._velocity = None
+        else:
+            self._update(
+                jnp.array(self._historical_3d_poses),
+                jnp.array(self._historical_timestamps),
+            )
+
+    def predict(self, timestamp: datetime) -> TrackingPrediction:
+        if not self._historical_3d_poses:
+            raise ValueError("No historical 3D poses available for prediction")
+
+        # use the latest historical detection
+        latest_3d_pose = self._historical_3d_poses[-1]
+        latest_timestamp = self._historical_timestamps[-1]
+
+        delta_t_s = (timestamp - latest_timestamp).total_seconds()
+        assert delta_t_s >= 0, f"delta_t_s is negative: {delta_t_s}"
+
+        if self._velocity is None:
+            return TrackingPrediction(
+                velocity=None,
+                keypoints=latest_3d_pose,
+            )
+        else:
+            # Linear motion model: ẋt = xt' + Vt' · (t - t')
+            predicted_3d_pose = latest_3d_pose + self._velocity * delta_t_s
+            return TrackingPrediction(
+                velocity=self._velocity, keypoints=predicted_3d_pose
+            )
+
+    @jaxtyped(typechecker=beartype)
+    def _update(
+        self,
+        keypoints: Float[Array, "N J 3"],
+        timestamps: Float[Array, "N"],
+    ) -> None:
+        """
+        update measurements with least mean square method
+        """
+        if keypoints.shape[0] < 2:
+            raise ValueError("Not enough measurements to estimate velocity")
+
+        # Using least squares to fit a linear model for each joint and dimension
+        # X = timestamps, y = keypoints
+        # For each joint and each dimension, we solve for velocity
+
+        n_samples = timestamps.shape[0]
+        n_joints = keypoints.shape[1]
+
+        # Create design matrix for linear regression
+        # [t, 1] for each timestamp
+        X = jnp.column_stack([timestamps, jnp.ones(n_samples)])
+
+        # Reshape keypoints to solve for all joints and dimensions at once
+        # From [N, J, 3] to [N, J*3]
+        keypoints_reshaped = keypoints.reshape(n_samples, -1)
+
+        # Use JAX's lstsq to solve the least squares problem
+        coefficients, _, _, _ = jnp.linalg.lstsq(X, keypoints_reshaped, rcond=None)
+
+        # Coefficients shape is [2, J*3]
+        # First row: velocities, Second row: intercepts
+        velocities = coefficients[0].reshape(n_joints, 3)
+
+        # Update velocity
+        self._velocity = velocities
+
+    def update(self, keypoints: Float[Array, "J 3"], timestamp: datetime) -> None:
+        last_timestamp = self._historical_timestamps[-1]
+        assert last_timestamp <= timestamp
+
+        # deque would manage the maxlen automatically
+        self._historical_3d_poses.append(keypoints)
+        self._historical_timestamps.append(timestamp)
+
+        t_0 = self._historical_timestamps[0]
+        all_keypoints = jnp.array(self._historical_3d_poses)
+
+        def timestamp_to_seconds(timestamp: datetime) -> float:
+            assert t_0 <= timestamp
+            return (timestamp - t_0).total_seconds()
+
+        # timestamps relative to t_0 (the oldest detection timestamp)
+        all_timestamps = jnp.array(
+            map(timestamp_to_seconds, self._historical_timestamps)
+        )
+
+        self._update(all_keypoints, all_timestamps)
+
+    def get(self) -> TrackingPrediction:
+        if not self._historical_3d_poses:
+            raise ValueError("No historical 3D poses available")
+
+        latest_3d_pose = self._historical_3d_poses[-1]
+
+        if self._velocity is None:
+            return TrackingPrediction(velocity=None, keypoints=latest_3d_pose)
+        else:
+            return TrackingPrediction(velocity=self._velocity, keypoints=latest_3d_pose)
+
+
+class OneEuroFilter(GenericVelocityFilter):
+    """
+    Implementation of the 1€ filter (One Euro Filter) for smoothing keypoint data.
+
+    The 1€ filter is an adaptive low-pass filter that adjusts its cutoff frequency
+    based on movement speed to reduce jitter during slow movements while maintaining
+    responsiveness during fast movements.
+
+    Reference: https://cristal.univ-lille.fr/~casiez/1euro/
+    """
+
+    _x_filtered: Float[Array, "J 3"]
+    _dx_filtered: Optional[Float[Array, "J 3"]] = None
+    _last_timestamp: datetime
+    _min_cutoff: float
+    _beta: float
+    _d_cutoff: float
+
+    def __init__(
+        self,
+        keypoints: Float[Array, "J 3"],
+        timestamp: datetime,
+        min_cutoff: float = 1.0,
+        beta: float = 0.0,
+        d_cutoff: float = 1.0,
+    ):
+        """
+        Initialize the One Euro Filter.
+
+        Args:
+            keypoints: Initial keypoints positions
+            timestamp: Initial timestamp
+            min_cutoff: Minimum cutoff frequency (lower = more smoothing)
+            beta: Speed coefficient (higher = less lag during fast movements)
+            d_cutoff: Cutoff frequency for the derivative filter
+        """
+        self._last_timestamp = timestamp
+
+        # Filter parameters
+        self._min_cutoff = min_cutoff
+        self._beta = beta
+        self._d_cutoff = d_cutoff
+
+        # Filter state
+        self._x_filtered = keypoints  # Position filter state
+        self._dx_filtered = None  # Initially no velocity estimate
+
+    @overload
+    def _smoothing_factor(self, cutoff: float, dt: float) -> float: ...
+
+    @overload
+    def _smoothing_factor(
+        self, cutoff: Float[Array, "J"], dt: float
+    ) -> Float[Array, "J"]: ...
+
+    @jaxtyped(typechecker=beartype)
+    def _smoothing_factor(
+        self, cutoff: Union[float, Float[Array, "J"]], dt: float
+    ) -> Union[float, Float[Array, "J"]]:
+        """Calculate the smoothing factor for the low-pass filter."""
+        r = 2 * jnp.pi * cutoff * dt
+        return r / (r + 1)
+
+    @jaxtyped(typechecker=beartype)
+    def _exponential_smoothing(
+        self,
+        a: Union[float, Float[Array, "J"]],
+        x: Float[Array, "J 3"],
+        x_prev: Float[Array, "J 3"],
+    ) -> Float[Array, "J 3"]:
+        """Apply exponential smoothing to the input."""
+        return a * x + (1 - a) * x_prev
+
+    def predict(self, timestamp: datetime) -> TrackingPrediction:
+        """
+        Predict keypoints position at a given timestamp.
+
+        Args:
+            timestamp: Timestamp for prediction
+
+        Returns:
+            TrackingPrediction with velocity and keypoints
+        """
+        dt = (timestamp - self._last_timestamp).total_seconds()
+
+        if self._dx_filtered is None:
+            return TrackingPrediction(
+                velocity=None,
+                keypoints=self._x_filtered,
+            )
+        else:
+            predicted_keypoints = self._x_filtered + self._dx_filtered * dt
+            return TrackingPrediction(
+                velocity=self._dx_filtered,
+                keypoints=predicted_keypoints,
+            )
+
+    def update(self, keypoints: Float[Array, "J 3"], timestamp: datetime) -> None:
+        """
+        Update the filter with new measurements.
+
+        Args:
+            keypoints: New keypoint measurements
+            timestamp: Timestamp of the measurements
+        """
+        dt = (timestamp - self._last_timestamp).total_seconds()
+        if dt <= 0:
+            raise ValueError(
+                f"new timestamp is not greater than the last timestamp; expecting: {timestamp} > {self._last_timestamp}"
+            )
+
+        dx = (keypoints - self._x_filtered) / dt
+
+        # Determine cutoff frequency based on movement speed
+        cutoff = self._min_cutoff + self._beta * jnp.linalg.norm(
+            dx, axis=-1, keepdims=True
+        )
+
+        # Apply low-pass filter to velocity
+        a_d = self._smoothing_factor(self._d_cutoff, dt)
+        self._dx_filtered = self._exponential_smoothing(
+            a_d,
+            dx,
+            (
+                jnp.zeros_like(keypoints)
+                if self._dx_filtered is None
+                else self._dx_filtered
+            ),
+        )
+
+        # Apply low-pass filter to position with adaptive cutoff
+        a_cutoff = self._smoothing_factor(jnp.asarray(cutoff), dt)
+        self._x_filtered = self._exponential_smoothing(
+            a_cutoff, keypoints, self._x_filtered
+        )
+
+        # Update timestamp
+        self._last_timestamp = timestamp
+
+    def get(self) -> TrackingPrediction:
+        """
+        Get the current state of the filter.
+
+        Returns:
+            TrackingPrediction with velocity and keypoints
+        """
+        return TrackingPrediction(
+            velocity=self._dx_filtered,
+            keypoints=self._x_filtered,
+        )
 
 
 @jaxtyped(typechecker=beartype)
 @dataclass(frozen=True)
-class Tracking:
+class TrackingState:
-    id: int
     """
-    The tracking id
+    immutable state of a tracking
     """
+
     keypoints: Float[Array, "J 3"]
     """
     The 3D keypoints of the tracking
@@ -41,50 +456,97 @@ class Tracking:
     The last active timestamp of the tracking
     """
 
-    historical_detections: PVector[Detection]
+    historical_detections_by_camera: PMap[CameraID, Detection]
     """
     Historical detections of the tracking.
 
     Used for 3D re-triangulation
     """
 
-    velocity: Optional[Float[Array, "3"]] = None
-    """
-    Could be `None`. Like when the 3D pose is initialized.
 
-    `velocity` should be updated when target association yields a new
+class Tracking:
-    3D pose.
+    id: TrackingID
-    """
+    state: TrackingState
+    velocity_filter: GenericVelocityFilter
+
+    def __init__(
+        self,
+        id: TrackingID,
+        state: TrackingState,
+        velocity_filter: Optional[GenericVelocityFilter] = None,
+    ):
+        self.id = id
+        self.state = state
+        self.velocity_filter = velocity_filter or DummyVelocityFilter(state.keypoints)
 
     def __repr__(self) -> str:
-        return f"Tracking({self.id}, {self.last_active_timestamp})"
+        return f"Tracking({self.id}, {self.state.last_active_timestamp})"
+
+    @overload
+    def predict(self, time: float) -> Float[Array, "J 3"]:
+        """
+        predict the keypoints at a given time
+
+        Args:
+            time: the time in seconds to predict the keypoints
+
+        Returns:
+            the predicted keypoints
+        """
+        ...  # pylint: disable=unnecessary-ellipsis
+
+    @overload
+    def predict(self, time: timedelta) -> Float[Array, "J 3"]:
+        """
+        predict the keypoints at a given time
+
+        Args:
+            time: the time delta to predict the keypoints
+        """
+        ...  # pylint: disable=unnecessary-ellipsis
+
+    @overload
+    def predict(self, time: datetime) -> Float[Array, "J 3"]:
+        """
+        predict the keypoints at a given time
+
+        Args:
+            time: the timestamp to predict the keypoints
+        """
+        ...  # pylint: disable=unnecessary-ellipsis
 
     def predict(
         self,
-        delta_t_s: float,
+        time: float | timedelta | datetime,
     ) -> Float[Array, "J 3"]:
-        """
+        if isinstance(time, timedelta):
-        Predict the 3D pose of a tracking based on its velocity.
+            timestamp = self.state.last_active_timestamp + time
-        JAX-friendly implementation that avoids Python control flow.
+        elif isinstance(time, datetime):
-
+            timestamp = time
-        Args:
-            delta_t_s: Time delta in seconds
-
-        Returns:
-            Predicted 3D pose keypoints
-        """
-        # ------------------------------------------------------------------
-        # Step 1 – decide velocity on the Python side
-        # ------------------------------------------------------------------
-        if self.velocity is None:
-            velocity = jnp.zeros_like(self.keypoints)  # (J, 3)
         else:
-            velocity = self.velocity  # (J, 3)
+            timestamp = self.state.last_active_timestamp + timedelta(seconds=time)
+        # pylint: disable-next=unsubscriptable-object
+        return self.velocity_filter.predict(timestamp)["keypoints"]
 
-        # ------------------------------------------------------------------
+    def update(self, new_3d_pose: Float[Array, "J 3"], timestamp: datetime) -> None:
-        # Step 2 – pure JAX math
+        """
-        # ------------------------------------------------------------------
+        update the tracking with a new 3D pose
-        return self.keypoints + velocity * delta_t_s
+
+        Note:
+            equivalent to call `velocity_filter.update(new_3d_pose, timestamp)`
+        """
+        self.velocity_filter.update(new_3d_pose, timestamp)
+
+    @property
+    def velocity(self) -> Float[Array, "J 3"]:
+        """
+        The velocity of the tracking for each keypoint
+        """
+        # pylint: disable-next=unsubscriptable-object
+        if (vel := self.velocity_filter.get()["velocity"]) is None:
+            return jnp.zeros_like(self.state.keypoints)
+        else:
+            return vel
 
 
 @jaxtyped(typechecker=beartype)
@@ -100,7 +562,7 @@ class AffinityResult:
     indices_T: Int[Array, "T"]  # pylint: disable=invalid-name
     indices_D: Int[Array, "D"]  # pylint: disable=invalid-name
 
-    def tracking_detections(
+    def tracking_association(
         self,
     ) -> Generator[tuple[float, Tracking, Detection], None, None]:
         """

playground.py

@@ -31,13 +31,13 @@ from typing import (
     TypeVar,
     cast,
     overload,
+    Iterable,
 )
 
 import awkward as ak
 import jax
 import jax.numpy as jnp
 import numpy as np
-import orjson
 from beartype import beartype
 from beartype.typing import Mapping, Sequence
 from cv2 import undistortPoints
@@ -46,9 +46,10 @@ from jaxtyping import Array, Float, Num, jaxtyped
 from matplotlib import pyplot as plt
 from numpy.typing import ArrayLike
 from optax.assignment import hungarian_algorithm as linear_sum_assignment
-from pyrsistent import v, pvector
+from pyrsistent import pvector, v, m, pmap, PMap, freeze, thaw
 from scipy.spatial.transform import Rotation as R
 from typing_extensions import deprecated
+from collections import defaultdict
 
 from app.camera import (
     Camera,
@@ -59,15 +60,22 @@ from app.camera import (
     classify_by_camera,
 )
 from app.solver._old import GLPKSolver
-from app.tracking import AffinityResult, Tracking
+from app.tracking import (
+    TrackingID,
+    AffinityResult,
+    LastDifferenceVelocityFilter,
+    LeastMeanSquareVelocityFilter,
+    Tracking,
+    TrackingState,
+)
 from app.visualize.whole_body import visualize_whole_body
 
 NDArray: TypeAlias = np.ndarray
 
 # %%
 DATASET_PATH = Path("samples") / "04_02"
-AK_CAMERA_DATASET: ak.Array = ak.from_parquet(DATASET_PATH / "camera_params.parquet")
+AK_CAMERA_DATASET: ak.Array = ak.from_parquet(DATASET_PATH / "camera_params.parquet")  # type: ignore
-DELTA_T_MIN = timedelta(milliseconds=10)
+DELTA_T_MIN = timedelta(milliseconds=1)
 display(AK_CAMERA_DATASET)
 
 
@@ -431,12 +439,12 @@ def triangulate_one_point_from_multiple_views_linear(
 ) -> Float[Array, "3"]:
     """
     Args:
-        proj_matrices: 形状为(N, 3, 4)的投影矩阵序列
+        proj_matrices: (N, 3, 4) projection matrices
-        points: 形状为(N, 2)的点坐标序列
+        points:       (N, 2) image-coordinates per view
-        confidences: 形状为(N,)的置信度序列，范围[0.0, 1.0]
+        confidences:  (N,) optional per-view confidences in [0,1]
 
     Returns:
-        point_3d: 形状为(3,)的三角测量得到的3D点
+        (3,) 3D point
     """
     assert len(proj_matrices) == len(points)
 
@@ -462,7 +470,7 @@ def triangulate_one_point_from_multiple_views_linear(
 
     # replace the Python `if` with a jnp.where
     point_3d_homo = jnp.where(
-        point_3d_homo[3] < 0,  # predicate (scalar bool tracer)
+        point_3d_homo[3] <= 0,  # predicate (scalar bool tracer)
         -point_3d_homo,  # if True
         point_3d_homo,  # if False
     )
@@ -486,14 +494,14 @@ def triangulate_points_from_multiple_views_linear(
         confidences:  (N, P, 1) optional per-view confidences in [0,1]
 
     Returns:
-        (P, 3) 3D point for each of the P tracks
+        (P, 3) 3D point for each of the P
     """
     N, P, _ = points.shape
     assert proj_matrices.shape[0] == N
     if confidences is None:
         conf = jnp.ones((N, P), dtype=jnp.float32)
     else:
-        conf = jnp.sqrt(jnp.clip(confidences, 0.0, 1.0))
+        conf = confidences
 
     # vectorize your one-point routine over P
     vmap_triangulate = jax.vmap(
@@ -504,6 +512,142 @@ def triangulate_points_from_multiple_views_linear(
     return vmap_triangulate(proj_matrices, points, conf)
 
 
+# %%
+@jaxtyped(typechecker=beartype)
+def triangulate_one_point_from_multiple_views_linear_time_weighted(
+    proj_matrices: Float[Array, "N 3 4"],
+    points: Num[Array, "N 2"],
+    delta_t: Num[Array, "N"],
+    lambda_t: float = 10.0,
+    confidences: Optional[Float[Array, "N"]] = None,
+) -> Float[Array, "3"]:
+    """
+    Triangulate one point from multiple views with time-weighted linear least squares.
+
+    Implements the incremental reconstruction method from "Cross-View Tracking for Multi-Human 3D Pose"
+    with weighting formula: w_i = exp(-λ_t(t-t_i)) / ||c^i^T||_2
+
+    Args:
+        proj_matrices: Shape (N, 3, 4) projection matrices sequence
+        points: Shape (N, 2) point coordinates sequence
+        delta_t: Time differences between current time and each observation (in seconds)
+        lambda_t: Time penalty rate (higher values decrease influence of older observations)
+        confidences: Shape (N,) confidence values in range [0.0, 1.0]
+
+    Returns:
+        point_3d: Shape (3,) triangulated 3D point
+    """
+    assert len(proj_matrices) == len(points)
+    assert len(delta_t) == len(points)
+
+    N = len(proj_matrices)
+
+    # Prepare confidence weights
+    confi: Float[Array, "N"]
+    if confidences is None:
+        confi = jnp.ones(N, dtype=np.float32)
+    else:
+        confi = jnp.sqrt(jnp.clip(confidences, 0, 1))
+
+    A = jnp.zeros((N * 2, 4), dtype=np.float32)
+
+    # First build the coefficient matrix without weights
+    for i in range(N):
+        x, y = points[i]
+        A = A.at[2 * i].set(proj_matrices[i, 2] * x - proj_matrices[i, 0])
+        A = A.at[2 * i + 1].set(proj_matrices[i, 2] * y - proj_matrices[i, 1])
+
+    # Then apply the time-based and confidence weights
+    for i in range(N):
+        # Calculate time-decay weight: e^(-λ_t * Δt)
+        time_weight = jnp.exp(-lambda_t * delta_t[i])
+
+        # Calculate normalization factor: ||c^i^T||_2
+        row_norm_1 = jnp.linalg.norm(A[2 * i])
+        row_norm_2 = jnp.linalg.norm(A[2 * i + 1])
+
+        # Apply combined weight: time_weight / row_norm * confidence
+        w1 = (time_weight / row_norm_1) * confi[i]
+        w2 = (time_weight / row_norm_2) * confi[i]
+
+        A = A.at[2 * i].mul(w1)
+        A = A.at[2 * i + 1].mul(w2)
+
+    # Solve using SVD
+    _, _, vh = jnp.linalg.svd(A, full_matrices=False)
+    point_3d_homo = vh[-1]  # shape (4,)
+
+    # Ensure homogeneous coordinate is positive
+    point_3d_homo = jnp.where(
+        point_3d_homo[3] <= 0,
+        -point_3d_homo,
+        point_3d_homo,
+    )
+
+    # Convert from homogeneous to Euclidean coordinates
+    point_3d = point_3d_homo[:3] / point_3d_homo[3]
+    return point_3d
+
+
+@jaxtyped(typechecker=beartype)
+def triangulate_points_from_multiple_views_linear_time_weighted(
+    proj_matrices: Float[Array, "N 3 4"],
+    points: Num[Array, "N P 2"],
+    delta_t: Num[Array, "N"],
+    lambda_t: float = 10.0,
+    confidences: Optional[Float[Array, "N P"]] = None,
+) -> Float[Array, "P 3"]:
+    """
+    Vectorized version that triangulates P points from N camera views with time-weighting.
+
+    This function uses JAX's vmap to efficiently triangulate multiple points in parallel.
+
+    Args:
+        proj_matrices: Shape (N, 3, 4) projection matrices for N cameras
+        points: Shape (N, P, 2) 2D points for P keypoints across N cameras
+        delta_t: Shape (N,) time differences between current time and each camera's timestamp (seconds)
+        lambda_t: Time penalty rate (higher values decrease influence of older observations)
+        confidences: Shape (N, P) confidence values for each point in each camera
+
+    Returns:
+        points_3d: Shape (P, 3) triangulated 3D points
+    """
+    N, P, _ = points.shape
+    assert (
+        proj_matrices.shape[0] == N
+    ), "Number of projection matrices must match number of cameras"
+    assert delta_t.shape[0] == N, "Number of time deltas must match number of cameras"
+
+    if confidences is None:
+        # Create uniform confidences if none provided
+        conf = jnp.ones((N, P), dtype=jnp.float32)
+    else:
+        conf = confidences
+
+    # Define the vmapped version of the single-point function
+    # We map over the second dimension (P points) of the input arrays
+    vmap_triangulate = jax.vmap(
+        triangulate_one_point_from_multiple_views_linear_time_weighted,
+        in_axes=(
+            None,
+            1,
+            None,
+            None,
+            1,
+        ),  # proj_matrices and delta_t static, map over points
+        out_axes=0,  # Output has first dimension corresponding to points
+    )
+
+    # For each point p, extract the 2D coordinates from all cameras and triangulate
+    return vmap_triangulate(
+        proj_matrices,  # (N, 3, 4) - static across points
+        points,  # (N, P, 2) - map over dim 1 (P)
+        delta_t,  # (N,) - static across points
+        lambda_t,  # scalar - static
+        conf,  # (N, P) - map over dim 1 (P)
+    )
+
+
 # %%
 
 
@@ -524,6 +668,23 @@ def triangle_from_cluster(
 
 
 # %%
+def group_by_cluster_by_camera(
+    cluster: Sequence[Detection],
+) -> PMap[CameraID, Detection]:
+    """
+    group the detections by camera, and preserve the latest detection for each camera
+    """
+    r: dict[CameraID, Detection] = {}
+    for el in cluster:
+        if el.camera.id in r:
+            eld = r[el.camera.id]
+            preserved = max([eld, el], key=lambda x: x.timestamp)
+            r[el.camera.id] = preserved
+        else:
+            r[el.camera.id] = el
+    return pmap(r)
+
+
 class GlobalTrackingState:
     _last_id: int
     _trackings: dict[int, Tracking]
@@ -542,13 +703,25 @@ class GlobalTrackingState:
         return shallow_copy(self._trackings)
 
     def add_tracking(self, cluster: Sequence[Detection]) -> Tracking:
+        if len(cluster) < 2:
+            raise ValueError(
+                "cluster must contain at least 2 detections to form a tracking"
+            )
         kps_3d, latest_timestamp = triangle_from_cluster(cluster)
         next_id = self._last_id + 1
-        tracking = Tracking(
+        tracking_state = TrackingState(
-            id=next_id,
             keypoints=kps_3d,
             last_active_timestamp=latest_timestamp,
-            historical_detections=v(*cluster),
+            historical_detections_by_camera=group_by_cluster_by_camera(cluster),
+        )
+        tracking = Tracking(
+            id=next_id,
+            state=tracking_state,
+            # velocity_filter=LastDifferenceVelocityFilter(kps_3d, latest_timestamp),
+            velocity_filter=LeastMeanSquareVelocityFilter(
+                historical_3d_poses=[kps_3d],
+                historical_timestamps=[latest_timestamp],
+            ),
         )
         self._trackings[next_id] = tracking
         self._last_id = next_id
@@ -671,11 +844,7 @@ def perpendicular_distance_camera_2d_points_to_tracking_raycasting(
         Array of perpendicular distances for each keypoint
     """
     camera = detection.camera
-    # Use the delta_t supplied by the caller, but clamp to DELTA_T_MIN to
+    predicted_pose = tracking.predict(delta_t)
-    # avoid division-by-zero / exploding affinities.
-    delta_t = max(delta_t, DELTA_T_MIN)
-    delta_t_s = delta_t.total_seconds()
-    predicted_pose = tracking.predict(delta_t_s)
 
     # Back-project the 2D points to 3D space
     # intersection with z=0 plane
@@ -755,12 +924,12 @@ def calculate_tracking_detection_affinity(
         Combined affinity score
     """
     camera = detection.camera
-    delta_t_raw = detection.timestamp - tracking.last_active_timestamp
+    delta_t_raw = detection.timestamp - tracking.state.last_active_timestamp
     # Clamp delta_t to avoid division-by-zero / exploding affinity.
     delta_t = max(delta_t_raw, DELTA_T_MIN)
 
     # Calculate 2D affinity
-    tracking_2d_projection = camera.project(tracking.keypoints)
+    tracking_2d_projection = camera.project(tracking.state.keypoints)
     w, h = camera.params.image_size
     distance_2d = calculate_distance_2d(
         tracking_2d_projection,
@@ -840,7 +1009,7 @@ def calculate_camera_affinity_matrix_jax(
 
     # === Tracking-side tensors ===
     kps3d_trk: Float[Array, "T J 3"] = jnp.stack(
-        [trk.keypoints for trk in trackings]
+        [trk.state.keypoints for trk in trackings]
     )  # (T, J, 3)
     J = kps3d_trk.shape[1]
     # === Detection-side tensors ===
@@ -857,12 +1026,12 @@ def calculate_camera_affinity_matrix_jax(
     # --- timestamps ----------
     t0 = min(
         chain(
-            (trk.last_active_timestamp for trk in trackings),
+            (trk.state.last_active_timestamp for trk in trackings),
             (det.timestamp for det in camera_detections),
         )
     ).timestamp()  # common origin (float)
     ts_trk = jnp.array(
-        [trk.last_active_timestamp.timestamp() - t0 for trk in trackings],
+        [trk.state.last_active_timestamp.timestamp() - t0 for trk in trackings],
         dtype=jnp.float32,  # now small, ms-scale fits in fp32
     )
     ts_det = jnp.array(
@@ -1033,8 +1202,82 @@ display(affinities)
 
 
 # %%
-def update_tracking(tracking: Tracking, detection: Detection):
+def affinity_result_by_tracking(
-    delta_t_ = detection.timestamp - tracking.last_active_timestamp
+    results: Iterable[AffinityResult],
-    delta_t = max(delta_t_, DELTA_T_MIN)
+    min_affinity: float = 0.0,
+) -> dict[TrackingID, list[Detection]]:
+    """
+    Group affinity results by target ID.
 
-    return tracking
+    Args:
+        results: the affinity results to group
+        min_affinity: the minimum affinity to consider
+    Returns:
+        a dictionary mapping tracking IDs to a list of detections
+    """
+    res: dict[TrackingID, list[Detection]] = defaultdict(list)
+    for affinity_result in results:
+        for affinity, t, d in affinity_result.tracking_association():
+            if affinity < min_affinity:
+                continue
+            res[t.id].append(d)
+    return res
+
+
+def update_tracking(
+    tracking: Tracking,
+    detections: Sequence[Detection],
+    max_delta_t: timedelta = timedelta(milliseconds=100),
+    lambda_t: float = 10.0,
+) -> None:
+    """
+    update the tracking with a new set of detections
+
+    Args:
+        tracking: the tracking to update
+        detections: the detections to update the tracking with
+        max_delta_t: the maximum time difference between the last active timestamp and the latest detection
+        lambda_t: the lambda value for the time difference
+
+    Note:
+        the function would mutate the tracking object
+    """
+    last_active_timestamp = tracking.state.last_active_timestamp
+    latest_timestamp = max(d.timestamp for d in detections)
+    d = thaw(tracking.state.historical_detections_by_camera)
+    for detection in detections:
+        d[detection.camera.id] = detection
+    for camera_id, detection in d.items():
+        if detection.timestamp - latest_timestamp > max_delta_t:
+            del d[camera_id]
+    new_detections = freeze(d)
+    new_detections_list = list(new_detections.values())
+    project_matrices = jnp.stack(
+        [detection.camera.params.projection_matrix for detection in new_detections_list]
+    )
+    delta_t = jnp.array(
+        [
+            detection.timestamp.timestamp() - last_active_timestamp.timestamp()
+            for detection in new_detections_list
+        ]
+    )
+    kps = jnp.stack([detection.keypoints for detection in new_detections_list])
+    conf = jnp.stack([detection.confidences for detection in new_detections_list])
+    kps_3d = triangulate_points_from_multiple_views_linear_time_weighted(
+        project_matrices, kps, delta_t, lambda_t, conf
+    )
+    new_state = TrackingState(
+        keypoints=kps_3d,
+        last_active_timestamp=latest_timestamp,
+        historical_detections_by_camera=new_detections,
+    )
+    tracking.update(kps_3d, latest_timestamp)
+    tracking.state = new_state
+
+
+# %%
+affinity_results_by_tracking = affinity_result_by_tracking(affinities.values())
+for tracking_id, detections in affinity_results_by_tracking.items():
+    update_tracking(global_tracking_state.trackings[tracking_id], detections)
+
+# %%