Multiobject Tracking via Discriminative Embeddings for the Internet of Things

Abstract

Multi-object tracking technology can be deployed to the IoT devices to enhance the security and reliability of some video analysis applications such as video surveillance and intelligent security system. However, since the IoT devices with limited computing capacity and storage, most existing multi-object tracking methods are difficult to deploy to IoT devices and exhibit poor tracking robustness in scenes with frequent occlusions, severe crowded and scale variations. To alleviate aforementioned issues, we propose a regression-based online multi-object tracking method. First, an object-aware embedding extraction module (OAEM) is designed to extract preliminary discriminative embedding of the object in current frame. And then an embedding aggregation module (EAM) is proposed to obtain high-quality aggregated embedding in temporal. Finally, the temporal embedding combined with the preliminary embedding extracted from the current frame to obtain a refined embedding feature for subsequent position prediction and association. Importantly, we achieve a beneficial interaction between embedding extraction, position prediction and association task. And the proposed method does not suffer from significant memory consumption. Therefore, our method is a potential solution for intelligent video analysis on IoT devices. To evaluate the proposed method, we have conducted numerous experiments on the MOT16, MOT17 and MOT20 benchmark datasets. Results demonstrate that the proposed method can provide a more robust tracking performance compared to other optimal methods.