GaitContour: Novel Gait Recognition Method with Contour-Pose Representation

Konsep Inti

Efficient gait recognition using a novel Contour-Pose representation.

Abstrak

Gait recognition based on walking patterns is more robust than appearance-based methods. The proposed GaitContour method leverages a point-based Contour-Pose representation to efficiently compute subject embeddings. By combining body shape and body parts information, GaitContour outperforms previous methods while being more efficient. The design significantly reduces complexity and improves performance through local-to-global architecture. Large-scale experiments demonstrate the effectiveness of GaitContour in challenging datasets with distractors.

Statistik

Parameters for GaitBase: 7.30M
Parameters for GPGait: 7.93M
Parameters for GaitContour: 0.662M

Kutipan

"Gait recognition holds the promise to robustly identify subjects based on walking patterns instead of appearance information."
"Through large scale experiments, GaitContour is shown to perform significantly better than previous point-based methods."
"GaitContour can even outperform silhouette-based methods on challenging datasets."

Wawasan Utama Disaring Dari

GaitContour

by Yuxiang Guo,... pada arxiv.org 03-15-2024

https://arxiv.org/pdf/2311.16497.pdf

Pertanyaan yang Lebih Dalam

How can the efficiency of gait recognition methods be further improved?

Efficiency in gait recognition methods can be enhanced through several strategies. One approach is to optimize the input representation, such as Contour-Pose, which efficiently combines silhouette and pose information while maintaining temporal consistency. Additionally, employing local-to-global architectures like GaitContour can help reduce computational complexity by focusing on relevant features at different levels. Furthermore, incorporating techniques like shared transformers for processing regional information and utilizing sinusoidal embeddings can improve efficiency without compromising performance. By carefully designing models that balance accuracy with computational cost, gait recognition methods can achieve higher efficiency.

What are the potential limitations of relying solely on contour-pose representations for gait recognition?

While Contour-Pose representations offer advantages in terms of compactness and rich information integration from silhouettes and pose keypoints, there are potential limitations to consider. One limitation is related to the complexity of defining consistent contour points across frames based on pose guidance. The ordering and selection of contour points may introduce errors if not accurately aligned with pose keypoints. Another limitation could arise from variations in body shapes or movements that may not be effectively captured by a fixed set of contour points derived from silhouettes. Additionally, relying solely on Contour-Pose representations may overlook certain subtle details present in full-body images or videos that could contribute to better identification accuracy.

How might the principles behind gait recognition be applied to other fields beyond biometric identification?

The principles underlying gait recognition, such as analyzing human movement patterns for identification purposes, have broader applications beyond biometric identification:

Healthcare: Gait analysis techniques could be utilized for monitoring and diagnosing various health conditions based on changes in an individual's walking patterns.
Sports Science: Gait analysis can aid athletes in optimizing their performance by identifying areas for improvement in running or walking techniques.
Rehabilitation: Gait analysis technology could assist physical therapists in assessing patients' progress during rehabilitation programs following injuries or surgeries.
Security: Beyond traditional biometrics, analyzing unique aspects of an individual's gait could enhance security measures for access control systems or surveillance applications.
Human-Computer Interaction (HCI): Incorporating gait recognition into HCI systems could enable more natural interactions between users and devices through gesture-based commands triggered by specific walking patterns.

By leveraging the principles of gait recognition across these diverse fields, innovative solutions can be developed to address various challenges and enhance existing technologies with human movement analysis capabilities.