Multiple-Object Tracking (MOT) is of crucial importance for applications such as retail video analytics and video surveillance. Object detectors are often the computational bottleneck of modern MOT systems, limiting their use for real-time applications. In this paper, we address this issue by leveraging on an ensemble of detectors, each running every f frames. We measured the performance of our system in the MOT16 benchmark. The proposed model surpassed other online entries of the MOT16 challenge in speed, while maintaining an acceptable accuracy.
@article{arxiv.1908.04349,
title = {A fast multi-object tracking system using an object detector ensemble},
author = {Richard Cobos and Jefferson Hernandez and Andres G. Abad},
journal= {arXiv preprint arXiv:1908.04349},
year = {2019}
}
Comments
5 pages, 4 figures, 1 table, published in 2019 IEEE Colombian Conference on Applications in Computational Intelligence (ColCACI)