AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time (2211.03375v1)

Abstract: Accurate whole-body multi-person pose estimation and tracking is an important yet challenging topic in computer vision. To capture the subtle actions of humans for complex behavior analysis, whole-body pose estimation including the face, body, hand and foot is essential over conventional body-only pose estimation. In this paper, we present AlphaPose, a system that can perform accurate whole-body pose estimation and tracking jointly while running in realtime. To this end, we propose several new techniques: Symmetric Integral Keypoint Regression (SIKR) for fast and fine localization, Parametric Pose Non-Maximum-Suppression (P-NMS) for eliminating redundant human detections and Pose Aware Identity Embedding for jointly pose estimation and tracking. During training, we resort to Part-Guided Proposal Generator (PGPG) and multi-domain knowledge distillation to further improve the accuracy. Our method is able to localize whole-body keypoints accurately and tracks humans simultaneously given inaccurate bounding boxes and redundant detections. We show a significant improvement over current state-of-the-art methods in both speed and accuracy on COCO-wholebody, COCO, PoseTrack, and our proposed Halpe-FullBody pose estimation dataset. Our model, source codes and dataset are made publicly available at https://github.com/MVIG-SJTU/AlphaPose.

• The paper introduces SIKR to reduce quantization errors, significantly improving fine-level keypoint estimation for face, hand, and foot.
• It incorporates P-NMS and pose-aware identity embedding to eliminate redundancy and accurately track individuals across frames.
• The system achieves state-of-the-art performance on benchmarks such as COCO and PoseTrack while operating in real time.

AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time

The paper presents AlphaPose, a comprehensive system developed for accurate whole-body multi-person pose estimation and tracking, achieving real-time performance. This work addresses the complexities of full-body pose estimation, which involves not only the body but also face, hand, and foot keypoints. The authors introduce several innovative techniques to enhance both estimation accuracy and system efficiency.

Key Contributions

1. Symmetric Integral Keypoint Regression (SIKR): The system incorporates SIKR, enabling precise keypoint localization by minimizing quantization errors inherent in traditional heatmap-based approaches. This technique ensures accuracy in fine-level areas such as face and hands.
2. Parametric Pose Non-Maximum-Suppression (P-NMS): P-NMS is introduced to manage redundant human detections effectively. It applies a novel metric for comparing pose similarity and eliminates redundant poses based on a learned threshold, enhancing detection accuracy and speed.
3. Pose Aware Identity Embedding & Tracking: By embedding pose-aware identity features, AlphaPose not only estimates poses but also tracks individuals across frames. This integration facilitates seamless tracking in dynamic scenes.
4. Part-Guided Proposal Generator (PGPG) and Multi-Domain Knowledge Distillation: These techniques expand training diversity by incorporating distinct body parts and transferring knowledge from various datasets, thus improving generalization and robustness.
5. Pipeline Optimization: The authors designed a multi-stage concurrent pipeline to optimize processing speed, allowing AlphaPose to operate at real-time speeds, even with complex data.

Strong Numerical Results and Validation

AlphaPose demonstrates significant improvements over existing state-of-the-art systems in both speed and accuracy across multiple benchmarks: COCO-wholebody, COCO, PoseTrack, and the authors' own Halpe-FullBody dataset. Specific advancements are seen in handling fine-grained keypoints with higher fidelity than traditional methods.

Implications and Future Directions

Practically, AlphaPose advances the field of computer vision by providing a more nuanced understanding of human actions in diverse applications such as human-computer interaction and behavioral analysis. Theoretically, it opens discussions for enhancing regression methodologies in pose estimation, particularly when dealing with multi-scale variations.

Future developments could focus on extending AlphaPose to include three-dimensional aspects, which would further enrich applications in areas like virtual reality and real-time 3D reconstruction. Additionally, integrating the system with edge computing technologies could broaden its applicability in mobile and IoT devices.

This paper represents a substantive contribution to the domain of pose estimation and tracking, providing an efficient and effective system well-suited for both academic exploration and practical deployment.