A fast multi-object tracking system using an object detector ensemble
This work addresses real-time performance issues in MOT for applications like retail video analytics and video surveillance, but it is incremental as it builds on existing ensemble methods.
The paper tackled the computational bottleneck of object detectors in multiple-object tracking systems by using an ensemble of detectors running every f frames, resulting in surpassing other online entries in the MOT16 benchmark in speed while maintaining acceptable accuracy.
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.