feat: Introduce Tracking class and refactor pose prediction

- Added a new `Tracking` class to encapsulate tracking data, including keypoints and velocity, with a method for predicting 3D poses based on velocity.
- Refactored the pose prediction logic in `calculate_camera_affinity_matrix_jax` to utilize the new `predict` method from the `Tracking` class, enhancing clarity and modularity.
- Introduced an `AffinityResult` class to manage results of affinity computations, including trackings and detections, improving the structure of the affinity calculation process.
- Updated imports and type hints for better organization and consistency across the codebase.

app/tracking/affinity_result.py

@@ -0,0 +1,37 @@
+from dataclasses import dataclass
+from typing import Sequence, Callable, Generator
+
+from app.camera import Detection
+from . import Tracking
+from beartype.typing import Sequence, Mapping
+from jaxtyping import jaxtyped, Float, Int
+from jax import Array
+
+
+@dataclass
+class AffinityResult:
+    """
+    Result of affinity computation between trackings and detections.
+    """
+
+    matrix: Float[Array, "T D"]
+    """
+    Affinity matrix between trackings and detections.
+    """
+
+    trackings: Sequence[Tracking]
+    """
+    Trackings used to compute the affinity matrix.
+    """
+
+    detections: Sequence[Detection]
+    """
+    Detections used to compute the affinity matrix.
+    """
+
+    indices_T: Sequence[int]
+    indices_D: Sequence[int]
+
+    def tracking_detections(self) -> Generator[tuple[Tracking, Detection]]:
+        for t, d in zip(self.indices_T, self.indices_D):
+            yield (self.trackings[t], self.detections[d])