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darkliang
2022-07-19 14:14:48 +08:00
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configs/gaitedge/README.md
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This [paper](https://arxiv.org/abs/2203.03972) has been accepted by ECCV 2022.
## Abstract
Gait is one of the most promising biometrics to identify individuals at a long distance. Although most previous methods have focused on recognizing the silhouettes, several end-to-end methods that extract gait features directly from RGB images perform better. However, we argue that these end-to-end methods inevitably suffer from the gait-unrelated noises, i.e., low-level texture and colorful information. Experimentally, we design both the cross-domain evaluation and visualization to stand for this view. In this work, we propose a novel end-to-end framework named GaitEdge which can effectively block gait-unrelated information and release end-to-end training potential. Specifically, GaitEdge synthesizes the output of the pedestrian segmentation network and then feeds it to the subsequent recognition network, where the synthetic silhouettes consist of trainable edges of bodies and fixed interiors to limit the information that the recognition network receives. Besides, GaitAlign for aligning silhouettes is embedded into the GaitEdge without loss of differentiability. Experimental results on CASIA-B and our newly built TTG-200 indicate that GaitEdge significantly outperforms the previous methods and provides a more practical end-to-end paradigm for blocking RGB noises effectively.
Gait is one of the most promising biometrics to identify individuals at a long distance. Although most previous methods have focused on recognizing the silhouettes, several end-to-end methods that extract gait features directly from RGB images perform better. However, we demonstrate that these end-to-end methods may inevitably suffer from the gait-irrelevant noises, i.e., low-level texture and colorful information. Experimentally, we design the **cross-domain** evaluation to support this view. In this work, we propose a novel end-to-end framework named **GaitEdge** which can effectively block gait-irrelevant information and release end-to-end training potential Specifically, GaitEdge synthesizes the output of the pedestrian segmentation network and then feeds it to the subsequent recognition network, where the synthetic silhouettes consist of trainable edges of bodies and fixed interiors to limit the information that the recognition network receives. Besides, **GaitAlign** for aligning silhouettes is embedded into the GaitEdge without losing differentiability. Experimental results on CASIA-B and our newly built TTG-200 indicate that GaitEdge significantly outperforms the previous methods and provides a more practical end-to-end paradigm.
![img](../../assets/gaitedge.png)
## CASIA-B*
Since the silhouettes of CASIA-B were obtained by the outdated background subtraction, there exists much noise caused by the background and clothes of subjects. Hence, we re-annotate the
silhouettes of CASIA-B and denote it as CASIA-B*. Refer to [here](../../datasets/CASIA-B*/README.md) for more details.
## Performance
| Model | NM | BG | CL | TTG-200 (cross-domain) | Configuration |
|:----------:|:----:|:----:|:----:|:----------------------:|:----------------------------------------------:|
| GaitGL | 94.0 | 89.6 | 81.0 | 53.2 | [phase1_rec.yaml](./phase1_rec.yaml) |
| GaitGL-E2E | 99.1 | 98.2 | 89.1 | 45.6 | [phase2_e2e.yaml](./phase2_e2e.yaml) |
| GaitEdge | 98.0 | 96.3 | 88.0 | 53.9 | [phase2_gaitedge.yaml](./phase2_gaitedge.yaml) |
***The results here are higher than those in the paper because we use a different optimization strategy. But this does not affect the conclusion of the paper.***
configs/gaitedge/phase1_rec.yaml
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# Note : *** the batch_size should be equal to the gpus number at the test phase!!! ***
data_cfg:
dataset_name: CASIA-B_new
dataset_name: CASIA-B*
dataset_root: your_path
dataset_partition: ./datasets/CASIA-B*/CASIA-B*.json
num_workers: 1
configs/gaitedge/phase1_seg.yaml
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# Note : *** the batch_size should be equal to the gpus number at the test phase!!! ***
data_cfg:
dataset_name: CASIA-B_new
dataset_name: CASIA-B*
dataset_root: your_path
dataset_partition: ./datasets/CASIA-B*/CASIA-B*.json
num_workers: 1
configs/gaitedge/phase2_e2e.yaml
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data_cfg:
dataset_name: CASIA-B_new
dataset_name: CASIA-B*
dataset_root: your_path
dataset_partition: ./datasets/CASIA-B*/CASIA-B*.json
num_workers: 1
configs/gaitedge/phase2_gaitedge.yaml
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data_cfg:
dataset_name: CASIA-B_new
dataset_name: CASIA-B*
dataset_root: your_path
dataset_partition: ./datasets/CASIA-B*/CASIA-B*.json
num_workers: 1
opengait/modeling/models/gaitedge.py
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@@ -4,7 +4,7 @@ import torch.optim as optim
from ..base_model import BaseModel
from .gaitgl import GaitGL
from ..modules import SilhouetteCropAndResize
from ..modules import GaitAlign
from torchvision.transforms import Resize
from utils import get_valid_args, get_attr_from, is_list_or_tuple
import os.path as osp
@@ -46,10 +46,12 @@ class GaitEdge(GaitGL):
super(GaitEdge, self).build_network(model_cfg["GaitGL"])
self.Backbone = self.get_backbone(model_cfg['Segmentation'])
self.align = model_cfg['align']
self.CROP = SilhouetteCropAndResize()
self.gait_align = GaitAlign()
self.resize = Resize((64, 44))
self.is_edge = model_cfg['edge']
self.seg_lr = model_cfg['seg_lr']
self.kernel = torch.ones(
(model_cfg['kernel_size'], model_cfg['kernel_size'])).cuda()
def finetune_parameters(self):
fine_tune_params = list()
@@ -88,14 +90,22 @@ class GaitEdge(GaitGL):
"Error type for -Restore_Hint-, supported: int or string.")
self._load_ckpt(save_name)
def preprocess(self, sils):
dilated_mask = (morph.dilation(sils, self.kernel).detach()
) > 0.5 # Dilation
eroded_mask = (morph.erosion(sils, self.kernel).detach()
) > 0.5 # Dilation
edge_mask = dilated_mask ^ eroded_mask
return edge_mask, eroded_mask
def forward(self, inputs):
ipts, labs, _, _, seqL = inputs
ratios = ipts[0]
rgbs = ipts[1]
sils = ipts[2]
# if len(sils.size()) == 4:
# sils = sils.unsqueeze(2)
n, s, c, h, w = rgbs.size()
rgbs = rgbs.view(n*s, c, h, w)
sils = sils.view(n*s, 1, h, w)
@@ -103,24 +113,19 @@ class GaitEdge(GaitGL):
logits = torch.sigmoid(logis)
mask = torch.round(logits).float()
if self.is_edge:
kernel_1 = torch.ones((3, 3)).cuda()
kernel_2 = torch.ones((3, 3)).cuda()
dilated_mask = (morph.dilation(sils, kernel_1).detach()
) > 0.5 # Dilation
eroded_mask = (morph.erosion(sils, kernel_2).detach()
) > 0.5 # Dilation
edge_mask = dilated_mask ^ eroded_mask
edge_mask, eroded_mask = self.preprocess(sils)
# Gait Synthesis
new_logits = edge_mask*logits+eroded_mask*sils
if self.align:
cropped_logits = self.CROP(
cropped_logits = self.gait_align(
new_logits, sils, ratios)
else:
cropped_logits = self.resize(new_logits)
else:
if self.align:
cropped_logits = self.CROP(
cropped_logits = self.gait_align(
logits, mask, ratios)
else:
cropped_logits = self.resize(logits)
opengait/modeling/modules.py
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@@ -183,9 +183,9 @@ class BasicConv3d(nn.Module):
return outs
class SilhouetteCropAndResize(nn.Module):
class GaitAlign(nn.Module):
def __init__(self, H=64, W=44, eps=1, **kwargs):
super(SilhouetteCropAndResize, self).__init__()
super(GaitAlign, self).__init__()
self.H, self.W, self.eps = H, W, eps
self.Pad = nn.ZeroPad2d((int(self.W / 2), int(self.W / 2), 0, 0))
self.RoiPool = RoIAlign((self.H, self.W), 1, sampling_ratio=-1)