Detect-and-Track: Efficient Pose Estimation in Videos (1712.09184v2)

Abstract: This paper addresses the problem of estimating and tracking human body keypoints in complex, multi-person video. We propose an extremely lightweight yet highly effective approach that builds upon the latest advancements in human detection and video understanding. Our method operates in two-stages: keypoint estimation in frames or short clips, followed by lightweight tracking to generate keypoint predictions linked over the entire video. For frame-level pose estimation we experiment with Mask R-CNN, as well as our own proposed 3D extension of this model, which leverages temporal information over small clips to generate more robust frame predictions. We conduct extensive ablative experiments on the newly released multi-person video pose estimation benchmark, PoseTrack, to validate various design choices of our model. Our approach achieves an accuracy of 55.2% on the validation and 51.8% on the test set using the Multi-Object Tracking Accuracy (MOTA) metric, and achieves state of the art performance on the ICCV 2017 PoseTrack keypoint tracking challenge.

• The paper introduces a two-stage detect-and-track pipeline that enhances pose estimation by incorporating spatiotemporal cues.
• It extends Mask R-CNN into a 3D framework, achieving 55.2% and 51.8% MOTA on PoseTrack validation and test sets.
• It leverages a simple heuristic tracking algorithm, such as the Hungarian method, for efficient real-time keypoint association.

Overview of "Detect-and-Track: Efficient Pose Estimation in Videos"

The paper "Detect-and-Track: Efficient Pose Estimation in Videos" presents a two-stage approach developed to estimate and track human body keypoints in complex, multi-person video environments. The authors propose a lightweight yet effective methodology that builds on advancements in human detection and video understanding, specifically employing Mask R-CNN and its novel 3D extension to incorporate temporal information, enhancing pose predictions.

The first stage of the presented method involves pose estimation at the frame level, where the authors explore both standard Mask R-CNN and their proposed 3D Mask R-CNN model, which uses spatiotemporal convolutions over clips to allow for more robust keypoint prediction. The second stage utilizes a computationally efficient algorithmic approach for linking these predictions over time to generate continuous human pose tracks. This pipelined approach has shown to improve results, achieving an accuracy of 55.2% on the PoseTrack validation set and 51.8% on the test set using the MOTA metric, establishing state-of-the-art performance at the time of its evaluation.

Technical Approach

The paper introduces significant extensions to existing pose estimation models. The 3D Mask R-CNN is designed by inflating 2D convolutional operations into 3D to comprehend and act upon the temporal associations within video frames. Two types of kernel initializations are compared: mean initialization and center initialization, with the latter providing superior results empirically.

To facilitate efficient tracking over time, post-processing of the frame-level predictions employs a tracking mechanism that incorporates a data association problem, addressed through simple heuristic algorithms such as the Hungarian algorithm. This allows the model to effectively track keypoint trajectories by associating detections derived from separate frames while maintaining computational efficiency, processing videos several orders of magnitude faster than prior state-of-the-art methods based on integer programming formulations.

Results and Implications

The procedure is rigorously validated on the PoseTrack dataset, demonstrating its scalability across complex scenes with multiple, potentially overlapping, human instances. Extensive ablative studies reveal the importance of several novel model design choices, such as the use of the 3D Mask R-CNN and the heuristic tracking algorithms. Further analysis provides insights into the upper bounds of the approach, indicating significant improvements by perfecting pose estimation and associating tracks optimally.

Despite its robustness, certain situations, such as handling occlusions or improving the quality of pose predictions particularly for small and low-resolution keypoints, remain challenging. The authors have also noted considerable run-time efficiency improvements, presenting a compelling case for practical applications in real-time scenarios.

Future Directions

The research opens avenues for further exploration, utilizing multi-GPU setups to enhance the applicability of 3D Mask R-CNNs on high-resolution, high-capacity model frameworks. As computational capabilities progress, the potential applications of such methodologies may expand to more intricate video analysis tasks, including those requiring dynamic scalability across longer video sequences while maintaining fine-grained pose tracking capabilities.

Overall, the contributions of this paper lie in effectively bridging the gap between intricate pose estimation accuracy and computational pragmatism, providing a robust framework that can inform and inspire continued innovations within pose estimation and video understanding domains.