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5 October 2021 Light and fast: multiple object tracking based on light-weight architecture
Gewei Su, Hongbo Zhao, Zhijun He, Zebin Sun
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Proceedings Volume 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning; 119110X (2021) https://doi.org/10.1117/12.2604710
Event: 2nd International Conference on Computer Vision, Image and Deep Learning, 2021, Liuzhou, China
Abstract
In recent years, there has been a great progress on object-tracking task. Most of them are based on Joint Detection and Embedding (JDE) benchmark, which accomplish detection and Re-ID task in a single module and thus could reduce the time cost and help to gain a higher processing FPS. However, large computation requirement of existing JDE-based method, which usually demand several expensive GPU devices, is still an obstacle to wide application for industry. In this paper, we propose a new lightweight structure named ShuffleXnet, and further build a simple module named Pyramid-ShuffleXnet (PSXnet) for Multiple-Object Tracking (MOT) task. The motivation of this work is to reduce the amount of calculation and make the network easier to be employed for online and real-time applications. Experimental results show that our method could achieve nearly 28% higher FPS than FairMOT with just 6.7% less by Multi-Object Tracking Accuracy (MOTA) score on MOT17 dataset.
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Gewei Su, Hongbo Zhao, Zhijun He, and Zebin Sun "Light and fast: multiple object tracking based on light-weight architecture", Proc. SPIE 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning, 119110X (5 October 2021); https://doi.org/10.1117/12.2604710
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KEYWORDS
Convolution
Feature extraction
Sensors
Network architectures
Target detection
Detection and tracking algorithms
Neural networks
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