WEPDTOF: A Dataset and Benchmark Algorithms for In-the-Wild People Detection and Tracking from Overhead Fisheye Cameras
M. Ozan Tezcan, Zhihao Duan, Mertcan Cokbas, Prakash Ishwar, Janusz Konrad
Abstract
Owing to their large field of view, overhead fisheye cameras are becoming a surveillance modality of choice for large indoor spaces. However, traditional people detection and tracking algorithms developed for side-mounted, rectilinear-lens cameras do not work well on images from overhead fisheye cameras due to their viewpoint and unique optics. While several people-detection algorithms have been recently developed for such cameras, they have all been tested on datasets consisting of "staged" recordings with a limited variety of people, scenes and challenges. Clearly, the performance of these algorithms "in the wild", i.e., on recordings with real-world challenges, remains un-known. In this paper, we introduce a new benchmark dataset of in-the-Wild Events for People Detection and Tracking from Overhead Fisheye cameras (WEPDTOF) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . The dataset features 14 YouTube videos captured in a wide range of scenes, 188 distinct person identities consistently labeled across time, and real-world challenges such as extreme occlusions and camouflage. Also, we propose 3 spatiotemporal extensions <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of a state-of-the-art people-detection algorithm to enhance the coherence of detections across time. Compared to top-performing algorithms, that are purely spatial, the new algorithms offer a significant performance improvement on the new dataset. Finally, we compare the people tracking performance of these algorithms on WEPDTOF.