Body-Part Joint Detection and Association via Extended Object Representation
This addresses the challenge of associating detected body parts with people in computer vision, which is important for applications like surveillance and human-computer interaction, but it is an incremental improvement over existing detection methods.
The paper tackles the problem of jointly detecting human bodies and their parts (e.g., face, head, hands) by proposing a novel extended object representation that integrates semantic and geometric information, achieving state-of-the-art association performance on benchmarks like CityPersons, CrowdHuman, and BodyHands while maintaining high detection accuracy.
The detection of human body and its related parts (e.g., face, head or hands) have been intensively studied and greatly improved since the breakthrough of deep CNNs. However, most of these detectors are trained independently, making it a challenging task to associate detected body parts with people. This paper focuses on the problem of joint detection of human body and its corresponding parts. Specifically, we propose a novel extended object representation that integrates the center location offsets of body or its parts, and construct a dense single-stage anchor-based Body-Part Joint Detector (BPJDet). Body-part associations in BPJDet are embedded into the unified representation which contains both the semantic and geometric information. Therefore, BPJDet does not suffer from error-prone association post-matching, and has a better accuracy-speed trade-off. Furthermore, BPJDet can be seamlessly generalized to jointly detect any body part. To verify the effectiveness and superiority of our method, we conduct extensive experiments on the CityPersons, CrowdHuman and BodyHands datasets. The proposed BPJDet detector achieves state-of-the-art association performance on these three benchmarks while maintains high accuracy of detection. Code is in https://github.com/hnuzhy/BPJDet.