Abstract
State-of-the-art (SoTA) detection-based tracking methods mostly accomplish the detection and the identification feature learning tasks separately. Only a few efforts include the joint learning of detection and identification features. This work proposes two novel one-stage trackers by introducing implicit and explicit attention to the tracking research topic. For our tracking system based on implicit attention, we further introduce a novel fusion of feature maps combining information from different abstraction levels. For our tracking system based on explicit attention, we introduce utilization of an additional auxiliary function. These systems outperform the SoTA tracking systems in terms of MOTP (Multi-Object Tracking Precision) and IDF1 score when evaluated on public benchmark datasets including MOT15, MOT16, and MOT17. High MOTP score indicates precise detection of bounding boxes of objects, while high IDF1 score indicates accurate ID detections, which is very crucial for surveillance and security systems. Therefore, proposed systems are good choice for event-detections in surveillance feeds as we are capable of detecting correct ID and precise location.
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This work was supported by the Milestone Research Programme at Aalborg University (MRPA).
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Madan, N., Nasrollahi, K., Moeslund, T.B. (2022). Attention-Enabled Object Detection to Improve One-Stage Tracker. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-030-82196-8_55
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