The WILDTRACK Multi-Camera Person Dataset
This provides a large-scale dataset for researchers working on multi-camera person detection and camera calibration, addressing challenges like occlusions, but it is incremental as it builds on existing datasets and methods.
The authors tackled the problem of multi-camera person detection by introducing the WILDTRACK dataset, which includes seven HD cameras capturing realistic scenarios and high-precision calibration, enabling improved detection performances through joint exploitation of across-view information.
People detection methods are highly sensitive to the perpetual occlusions among the targets. As multi-camera set-ups become more frequently encountered, joint exploitation of the across views information would allow for improved detection performances. We provide a large-scale HD dataset named WILDTRACK which finally makes advanced deep learning methods applicable to this problem. The seven-static-camera set-up captures realistic and challenging scenarios of walking people. Notably, its camera calibration with jointly high-precision projection widens the range of algorithms which may make use of this dataset. In aim to help accelerate the research on automatic camera calibration, such annotations also accompany this dataset. Furthermore, the rich-in-appearance visual context of the pedestrian class makes this dataset attractive for monocular pedestrian detection as well, since: the HD cameras are placed relatively close to the people, and the size of the dataset further increases seven-fold. In summary, we overview existing multi-camera datasets and detection methods, enumerate details of our dataset, and we benchmark multi-camera state of the art detectors on this new dataset.