Benchmarking the Complementary-View Multi-human Association and Tracking

• Published in: International journal of computer vision Vol. 132; no. 1; pp. 118 - 136
• Main Authors: Han, Ruize, Feng, Wei, Wang, Feifan, Qian, Zekun, Yan, Haomin, Wang, Song
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2024; Springer; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Benchmarks; Cameras; Computer Imaging; Computer Science; Computer vision; Datasets; Image Processing and Computer Vision; Pattern Recognition; Pattern Recognition and Graphics; Performance evaluation; Special Issue on Traditional Computer Vision in the Age of Deep Learning; Tracking; Vision; Benchmark; Multi-human association; Multi-human tracking; Complementary view
• ISSN: 0920-5691; 1573-1405
• DOI: 10.1007/s11263-023-01857-z

Using multiple moving cameras with different and time-varying views can significantly expand the capability of multiple human tracking in larger areas and with various perspectives. In particular, the use of moving cameras of complementary top and horizontal views can facilitate multi-human detection and tracking from both global and local perspectives. As a new challenging problem that draws more and more attention in recent years, one main issue is the lack of a comprehensive dataset for credible performance evaluation. In this paper, we present such a new dataset consisting of videos synchronously recorded by drone and wearable cameras, with high-quality annotations of the covered subjects and their cross-frame and cross-view associations. We also propose a pertinent baseline algorithm for multi-view multiple human tracking and evaluate it on this new dataset against the annotated ground truths. Experimental results verify the usefulness of the new dataset and the effectiveness of the proposed baseline algorithm.