통찰 - Computer Vision - # Gait Recognition from Compressed Videos

Improving Gait Recognition Accuracy in Highly Compressed Surveillance Videos through Task-Adapted Artifact Correction

Q: How can the proposed artifact correction model be extended to handle other types of video degradation beyond H.264 compression, such as motion blur, occlusion, and low resolution?

The proposed artifact correction model can be extended to handle various types of video degradation by incorporating additional modules or adjustments to the existing framework. To address motion blur, the model can be trained on datasets specifically designed to simulate motion blur effects on images. By introducing motion-blurred images along with their corresponding ground truth poses, the artifact correction model can learn to enhance the clarity of the blurred regions, thus improving pose estimation accuracy in such scenarios. For handling occlusion, the model can be trained on datasets containing images with varying degrees of occlusion on human subjects. By providing annotations for both visible and occluded body parts, the artifact correction model can learn to predict the occluded regions and fill in missing information, leading to more accurate pose estimations in occluded frames. To tackle low-resolution videos, the artifact correction model can be trained on datasets with images of different resolutions. By exposing the model to low-resolution images and their corresponding high-resolution versions, it can learn to enhance details and restore missing information, thereby improving pose estimation performance on low-resolution footage. By incorporating these additional training scenarios into the artifact correction model's training pipeline, it can learn to adapt to various types of video degradation beyond H.264 compression, making it a more robust and versatile tool for enhancing video quality for pose estimation tasks.

Q: Can the artifact correction model be further improved by incorporating additional training signals beyond just the pose estimation performance, such as gait recognition accuracy or other downstream task-specific metrics?

Yes, the artifact correction model can be further improved by incorporating additional training signals beyond pose estimation performance. By considering metrics related to downstream tasks such as gait recognition accuracy, the model can be optimized to enhance not only pose estimation but also the overall performance of gait analysis systems. Integrating gait recognition accuracy as a training signal can provide the artifact correction model with feedback on how well it improves the downstream task of gait recognition. By training the model to optimize for gait recognition accuracy in addition to pose estimation performance, it can learn to make corrections that are specifically tailored to enhance gait analysis results. Furthermore, incorporating other task-specific metrics related to gait analysis, such as walking speed classification or carrying condition recognition, can further guide the artifact correction model in making adjustments that are beneficial for a wider range of gait-related tasks. By leveraging multiple training signals from various downstream metrics, the model can be fine-tuned to improve the overall performance and robustness of gait analysis systems in real-world applications.

Q: What other computer vision tasks beyond gait recognition could benefit from the proposed two-stage approach of using a task-adapted artifact correction model in conjunction with a frozen base model?

The proposed two-stage approach of using a task-adapted artifact correction model in conjunction with a frozen base model can benefit various other computer vision tasks beyond gait recognition. Some of these tasks include: Human Action Recognition: By enhancing the quality of video frames and improving pose estimation accuracy, the approach can aid in recognizing complex human actions in videos, such as sports activities, dance performances, or sign language recognition. Surveillance Systems: The method can be applied to improve object detection, tracking, and behavior analysis in surveillance footage by enhancing image quality and enabling more accurate pose estimation of individuals in crowded or low-quality video scenarios. Medical Image Analysis: The approach can be utilized to enhance the quality of medical images for tasks like anatomical landmark detection, organ segmentation, and disease diagnosis, where accurate pose estimation or object localization is crucial. Autonomous Driving: Enhancing the quality of video frames in autonomous driving scenarios can improve object detection, lane tracking, and pedestrian pose estimation, leading to safer and more reliable autonomous navigation systems. Augmented Reality: By improving the accuracy of pose estimation in augmented reality applications, the approach can enhance virtual object placement, gesture recognition, and interactive experiences in AR environments. Overall, the two-stage approach of using a task-adapted artifact correction model can be applied to a wide range of computer vision tasks where accurate pose estimation and object localization are essential for achieving high-performance results.

핵심 개념

Incorporating a task-adapted artifact correction model can significantly improve the performance of pose estimation and downstream gait recognition in highly compressed surveillance videos, without compromising the model's generalization capabilities on high-quality data.

초록

The paper proposes a method to improve the performance of gait recognition in highly compressed surveillance videos. The key insights are:

Existing pose estimation models trained on high-quality datasets struggle to accurately estimate poses in low-quality, compressed surveillance footage due to the introduced artifacts.
Fine-tuning the pose estimation model on the low-quality data can improve its performance on the compressed videos, but this comes at the cost of degraded performance on the original high-quality data.
The authors introduce a two-stage approach that incorporates a separate artifact correction model, which is trained to optimize the performance of a frozen pose estimation model. This allows the pose estimation model to maintain its generalization capabilities while improving the accuracy on the low-quality compressed videos.
The authors automatically construct a dataset of low-quality videos and corresponding ground truth poses by applying H.264 compression to high-quality videos and using a state-of-the-art pose estimation model to obtain the ground truth.
Experiments on the PsyMo dataset show that the proposed approach outperforms both the pre-trained pose estimation model and the fine-tuned pose estimation model in terms of pose estimation accuracy on the compressed videos, as well as downstream gait recognition performance.
The artifact correction model is able to preserve the pose estimation model's performance on the original high-quality data, unlike the fine-tuned pose estimation model which suffers from catastrophic forgetting.

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arxiv.org

통계

"Pose estimation on the compressed test set: HRNet Base (AP 0.783), HRNet Fine-Tuned (AP 0.935), FBCNN Fine-Tuned + HRNet Base (AP 0.956)"
"Gait recognition accuracy on compressed videos: HRNet Base (32.33%), HRNet Fine-Tuned (41.44%), FBCNN Fine-Tuned + HRNet Base (47.33%)"
"Gait recognition accuracy on original high-quality videos: HRNet Base (42.35%), HRNet Fine-Tuned (40.35%), FBCNN Fine-Tuned + HRNet Base (45.73%)"

인용구

"Fine-tuning the pose estimation models increases the performance of the model on the highly degraded images to 0.935. Furthermore, it also slightly improves the AP on the original high quality images. However, our proposed two-stage method consisting of a fine-tuned artifact correction model in conjunction with a pre-trained pose estimation model obtains the highest Average Precision in both cases."
"The poses obtained with the fine-tuned artifact correction model in conjunction with the base pose estimation model yield the highest recognition performance in all scenarios of the testing set."
"The fine-tuned version of HRNet obtains the lowest overall performance with a mean recognition accuracy of 40.35%. This shows that directly fine-tuning the pose estimation model on low quality data negatively impacts its performance on the high quality data as the original model yields an accuracy of 42.35%."

핵심 통찰 요약

Gait Recognition from Highly Compressed Videos

by Andrei Nicul... 게시일 arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.12183.pdf

Gait Recognition from Highly Compressed Videos

더 깊은 질문

How can the proposed artifact correction model be extended to handle other types of video degradation beyond H.264 compression, such as motion blur, occlusion, and low resolution?

The proposed artifact correction model can be extended to handle various types of video degradation by incorporating additional modules or adjustments to the existing framework. To address motion blur, the model can be trained on datasets specifically designed to simulate motion blur effects on images. By introducing motion-blurred images along with their corresponding ground truth poses, the artifact correction model can learn to enhance the clarity of the blurred regions, thus improving pose estimation accuracy in such scenarios.
For handling occlusion, the model can be trained on datasets containing images with varying degrees of occlusion on human subjects. By providing annotations for both visible and occluded body parts, the artifact correction model can learn to predict the occluded regions and fill in missing information, leading to more accurate pose estimations in occluded frames.
To tackle low-resolution videos, the artifact correction model can be trained on datasets with images of different resolutions. By exposing the model to low-resolution images and their corresponding high-resolution versions, it can learn to enhance details and restore missing information, thereby improving pose estimation performance on low-resolution footage.
By incorporating these additional training scenarios into the artifact correction model's training pipeline, it can learn to adapt to various types of video degradation beyond H.264 compression, making it a more robust and versatile tool for enhancing video quality for pose estimation tasks.

Can the artifact correction model be further improved by incorporating additional training signals beyond just the pose estimation performance, such as gait recognition accuracy or other downstream task-specific metrics?

Yes, the artifact correction model can be further improved by incorporating additional training signals beyond pose estimation performance. By considering metrics related to downstream tasks such as gait recognition accuracy, the model can be optimized to enhance not only pose estimation but also the overall performance of gait analysis systems.
Integrating gait recognition accuracy as a training signal can provide the artifact correction model with feedback on how well it improves the downstream task of gait recognition. By training the model to optimize for gait recognition accuracy in addition to pose estimation performance, it can learn to make corrections that are specifically tailored to enhance gait analysis results.
Furthermore, incorporating other task-specific metrics related to gait analysis, such as walking speed classification or carrying condition recognition, can further guide the artifact correction model in making adjustments that are beneficial for a wider range of gait-related tasks. By leveraging multiple training signals from various downstream metrics, the model can be fine-tuned to improve the overall performance and robustness of gait analysis systems in real-world applications.

What other computer vision tasks beyond gait recognition could benefit from the proposed two-stage approach of using a task-adapted artifact correction model in conjunction with a frozen base model?

The proposed two-stage approach of using a task-adapted artifact correction model in conjunction with a frozen base model can benefit various other computer vision tasks beyond gait recognition. Some of these tasks include:

Human Action Recognition: By enhancing the quality of video frames and improving pose estimation accuracy, the approach can aid in recognizing complex human actions in videos, such as sports activities, dance performances, or sign language recognition.

Surveillance Systems: The method can be applied to improve object detection, tracking, and behavior analysis in surveillance footage by enhancing image quality and enabling more accurate pose estimation of individuals in crowded or low-quality video scenarios.

Medical Image Analysis: The approach can be utilized to enhance the quality of medical images for tasks like anatomical landmark detection, organ segmentation, and disease diagnosis, where accurate pose estimation or object localization is crucial.

Autonomous Driving: Enhancing the quality of video frames in autonomous driving scenarios can improve object detection, lane tracking, and pedestrian pose estimation, leading to safer and more reliable autonomous navigation systems.

Augmented Reality: By improving the accuracy of pose estimation in augmented reality applications, the approach can enhance virtual object placement, gesture recognition, and interactive experiences in AR environments.

Overall, the two-stage approach of using a task-adapted artifact correction model can be applied to a wide range of computer vision tasks where accurate pose estimation and object localization are essential for achieving high-performance results.