PoseTrack: A Benchmark for Human Pose Estimation and Tracking (1710.10000v2)

Abstract: Human poses and motions are important cues for analysis of videos with people and there is strong evidence that representations based on body pose are highly effective for a variety of tasks such as activity recognition, content retrieval and social signal processing. In this work, we aim to further advance the state of the art by establishing "PoseTrack", a new large-scale benchmark for video-based human pose estimation and articulated tracking, and bringing together the community of researchers working on visual human analysis. The benchmark encompasses three competition tracks focusing on i) single-frame multi-person pose estimation, ii) multi-person pose estimation in videos, and iii) multi-person articulated tracking. To facilitate the benchmark and challenge we collect, annotate and release a new %large-scale benchmark dataset that features videos with multiple people labeled with person tracks and articulated pose. A centralized evaluation server is provided to allow participants to evaluate on a held-out test set. We envision that the proposed benchmark will stimulate productive research both by providing a large and representative training dataset as well as providing a platform to objectively evaluate and compare the proposed methods. The benchmark is freely accessible at https://posetrack.net.

• The paper presents a comprehensive benchmark that addresses gaps in evaluating multi-person pose estimation and tracking with extensive, annotated video sequences.
• It defines three core tasks—single-frame estimation, video pose estimation, and articulated tracking—using rigorous metrics such as PCKh, MOTA, and MOTP.
• The results demonstrate that while current methods perform in controlled settings, they struggle in dynamic, crowded scenes, highlighting a need for improved temporal integration.

PoseTrack: A Benchmark for Human Pose Estimation and Tracking

The paper "PoseTrack: A Benchmark for Human Pose Estimation and Tracking" introduces a comprehensive large-scale benchmark aimed at advancing video-based human pose estimation and articulated tracking. Addressing a considerable gap in the evaluation of video pose estimation methods, this benchmark provides a dataset with detailed annotations for multi-person tracking in dynamic and crowded scenarios.

Contributions

The paper proposes three primary tasks for the benchmark:

1. Single-frame multi-person pose estimation: Evaluating the accuracy of detecting poses in individual frames without temporal context.
2. Multi-person pose estimation in videos: Enhancing single-frame pose predictions by leveraging video frames preceding and following the annotated ones.
3. Multi-person articulated tracking: Tracking individuals' poses consistently over time, focusing on both pose accuracy and temporal consistency.

The PoseTrack dataset significantly extends existing datasets both in scale and diversity. It comprises over 550 video sequences, thereby offering a substantial increase in the number of annotated frames and poses compared to prior works. The dense annotations include person tracks, identity markers, body joints, and ignore regions, fostering advanced evaluations over a wide array of real-world environments featuring varied activities and complex interactions.

Methodological Insights and Evaluations

The benchmark employs established metrics from multi-person pose estimation and multi-target tracking domains. The PCKh metric is employed for verifying joint localization accuracy, while MOTA and MOTP metrics assess temporal tracking precision. The evaluation protocol restricts the utilization of any ground-truth data during testing phases, mimicking real-world scenarios wherein such information is typically absent.

Notably, the authors assembled powerful baseline methods. The ArtTrack-baseline, for example, integrates the DeeperCut CNN architecture and a graph partitioning algorithm to perform articulated tracking, while PoseTrack-baseline leverages Part Affinity Fields with a graph model that prioritizes part-level tracking. These baselines facilitate comprehensive experimentations that depict the current capabilities and limitations of pose-tracking models.

Key Results and Discussion

The submission pool demonstrates that while existing approaches perform adequately under controlled settings with isolated individuals, they struggle with crowded scenes, occlusions, and dynamic changes. Tracking-by-detection paradigms dominate, often separating tasks of single-frame detection and temporal linkage. However, simple frame-to-frame associations show limitations under complex dynamics.

The reliance on external datasets for pre-training is significant, underscoring the diverse challenge scenarios that PoseTrack presents. Yet, no method has effectively harnessed temporal video data to enhance predictive modeling beyond basic detection paradigms.

Implications and Future Directions

The PoseTrack benchmark underscores the critical gaps between current multi-person pose estimation capabilities and the technological needs posed by real-world applications. It invites the exploration of deeper integration between detection and tracking, perhaps through end-to-end frameworks or innovations in temporal feature extraction. The benchmark is designed to stimulate advances in capturing articulated human motion, facing challenges like strong individual interactions and fast camera shifts, making it pertinent for applications in augmented/virtual reality, multimedia retrieval, and advanced human-computer interaction systems.

Conclusion

"PoseTrack: A Benchmark for Human Pose Estimation and Tracking" provides an essential resource for the computer vision community, setting a rigorous standard for developing and benchmarking human pose estimation systems in video data. Engaging with the challenging data and contexts represented within PoseTrack is anticipated to foster continued advancement in both theoretical and practical aspects of human pose tracking. The benchmark's open evaluation framework offers researchers a platform for objective assessment and comparative analysis, promoting progress in this evolving area of paper.