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DetectAndTrack is the reference implementation for the CVPR 2018 paper “Detect-and-Track: Efficient Pose Estimation in Videos,” focusing on human keypoint detection and tracking across video frames. The system combines per-frame pose detection with a tracking mechanism to maintain identities over time, enabling efficient multi-person pose estimation in video. Code and instructions are organized to replicate paper results and to serve as a starting point for researchers working on pose in video. Although the repo has been archived and is now read-only, its issue tracker and artifacts remain useful for understanding implementation details and experimental settings. The project sits alongside other Facebook Research vision efforts, offering historical context for the evolution of video pose and tracking techniques. Researchers can still study the algorithms, adapt the pipeline, or port ideas into modern frameworks.
Features
- Multi-person pose detection in videos
- Temporal tracking to maintain identities across frames
- Reference code aligned with the CVPR 2018 paper
- Scripts to reproduce evaluation and benchmarks
- Modular components for detection and tracking stages
- Read-only archival for stable, citable reference
Project Samples
