Enhancing Gait Recognition through Sequential Two-stream Skeleton Refinement

المفاهيم الأساسية

Gait recognition can be improved by fusing silhouette and skeleton representations, and refining the skeleton data using temporal consistency from silhouettes.

الملخص

The paper proposes GaitSTR, a method for gait recognition that combines silhouette and skeleton representations. The key insights are:

1. Silhouettes lack detailed part information when there is overlap between body segments, and are affected by carried objects and clothing. Skeletons provide more accurate part information but are sensitive to occlusions and low-quality images, causing inconsistencies in frame-wise results.

2. GaitSTR refines the skeleton representation by leveraging the temporal consistency between silhouettes and skeletons. It introduces two-level fusion: internal fusion within skeletons (between joints and bones) and cross-modal correction with temporal guidance from silhouettes.

3. The internal fusion uses self-correction residual blocks to improve consistency between joints and bones in the skeleton representation. The cross-modal fusion uses silhouette features to predict relative changes for joints and bones, refining the skeleton.

4. Experiments on four public gait recognition datasets show that the refined skeletons, when combined with silhouettes, outperform other state-of-the-art methods that use skeletons and silhouettes.

الإحصائيات

Gait recognition can achieve 98.4% rank-1 accuracy on the CASIA-B dataset, a 23.8% relative reduction in error rate compared to the best 2D convolution-based method.
Gait recognition can achieve 90.8% rank-1 accuracy on the OUMVLP dataset, a small improvement over the previous state-of-the-art.
Gait recognition can achieve 65.1% rank-1 accuracy on the Gait3D dataset, a 3.1% improvement over the previous state-of-the-art.
Gait recognition can achieve 89.6% rank-1 accuracy on the GREW dataset, a 3.9% improvement over the previous state-of-the-art.

اقتباسات

"Silhouettes suffer from variations due to clothing and carried objects, as shown in Figure 1 (a), introducing external ambiguity, with segmented parts of a binarized silhouette being unavailable."
"Skeletons, on the other hand, include inconsistencies across frames in a sequence due to erroneous joint predictions, as depicted in Figure 1 (b), thereby reducing the accuracy of gait recognition."
"We enhance the quality of skeletons by employing silhouettes to rectify the jitters while retaining necessary identity information for more accurate gait recognition."

الرؤى الأساسية المستخلصة من

GaitSTR

by Wanrong Zhen... في arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02345.pdf

استفسارات أعمق

How can the proposed skeleton refinement approach be extended to other applications beyond gait recognition, such as action recognition or pose estimation

The proposed skeleton refinement approach in gait recognition can be extended to other applications such as action recognition or pose estimation by leveraging the same principles of refining skeletal data for improved accuracy. In action recognition, the refined skeletons can help in capturing more precise joint movements and interactions, leading to better recognition of complex actions. By incorporating silhouettes or RGB images along with skeletons, the model can benefit from both detailed part information and overall body movements, enhancing the overall performance in action recognition tasks. Similarly, in pose estimation, the refined skeletons can provide more accurate joint positions, aiding in the estimation of body poses with higher precision. This can be particularly useful in applications where precise body pose information is crucial, such as in sports analytics or physical therapy monitoring.

What are the potential limitations of the cross-modal fusion approach, and how could it be further improved to handle more challenging scenarios, such as severe occlusions or large viewpoint changes

One potential limitation of the cross-modal fusion approach is its sensitivity to severe occlusions or large viewpoint changes, which can lead to inconsistencies in the fusion process. To address this, the approach could be further improved by incorporating robust feature extraction techniques that are resilient to occlusions and viewpoint changes. Techniques such as attention mechanisms or spatial-temporal graph convolutions can help in capturing relevant information from both modalities while mitigating the impact of occlusions or viewpoint changes. Additionally, introducing adaptive fusion strategies that dynamically adjust the fusion weights based on the quality and reliability of the input data can enhance the model's ability to handle challenging scenarios. By incorporating these enhancements, the cross-modal fusion approach can become more robust and effective in handling diverse and complex real-world scenarios.

Given the importance of temporal consistency in gait recognition, how could the proposed method be adapted to leverage additional temporal information, such as motion patterns or dynamics, to further enhance the recognition performance

To leverage additional temporal information such as motion patterns or dynamics in gait recognition, the proposed method can be adapted by incorporating recurrent neural networks (RNNs) or temporal convolutional networks (TCNs) to capture long-range temporal dependencies in the gait sequences. By integrating these temporal modeling techniques, the model can learn and exploit the temporal dynamics of gait patterns over time, leading to improved recognition performance. Furthermore, incorporating motion analysis techniques such as optical flow or spatiotemporal feature extraction can provide valuable insights into the dynamic aspects of gait, enabling the model to better differentiate between different individuals based on their unique motion patterns. By integrating these additional temporal cues into the existing framework, the proposed method can achieve enhanced recognition accuracy and robustness in gait recognition tasks.