A Dataset for Persistent Multi-Target Multi-Camera Tracking in RGB-D

Ryan Layne, Sion Hannuna, Massimo Camplani, Jake Hall, Timothy M. Hospedales, Tao Xiang, Majid Mirmehdi, Dima Damen; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 47-55

Video surveillance systems are now widely deployed to improve our lives by enhancing safety, security, health monitoring and business intelligence. This has motivated extensive research into automated video analysis. Nevertheless, there is a gap between the focus of contemporary research, and the needs of end users of video surveillance systems. Many existing benchmarks and methodologies focus on narrowly defined problems in detection, tracking, re-identification or recognition. In contrast, end users face higher-level problems such as long-term monitoring of identities in order to build a picture of a person's activity across the course of a day, producing usage statistics of a particular area of space, and that these capabilities should be robust to challenges such as change of clothing. To achieve this effectively requires less widely studied capabilities such as spatio-temporal reasoning about people identities and locations within a space partially observed by multiple cameras over an extended time period. To bridge this gap between research and required capabilities, we propose a new dataset LIMA that encompasses the challenges of monitoring a typical home / office environment. LIMA contains 4.5 hours of RGB-D video from three cameras monitoring a four room house. To reflect the challenges of a realistic practical application, the dataset includes clothes changes and visitors to ensure the global reasoning is a realistic open-set problem. In addition to raw data, we provide identity annotation for benchmarking, and tracking results from a contemporary RGB-D tracker - thus allowing focus on the higher level monitoring problems.