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Smart city article details

Title	Adaptive Multi-Vehicle Motion Counting
ID_Doc	6290
Authors	Nguyen X.-D.; Vu A.-K.N.; Nguyen T.-D.; Phan N.; Dinh B.-D.D.; Nguyen N.-D.; Nguyen T.V.; Nguyen V.-T.; Le D.-D.
Year	2022
Published	Signal, Image and Video Processing, 16, 8
DOI	http://dx.doi.org/10.1007/s11760-022-02184-5
Abstract	Counting multi-vehicle motions via traffic cameras in urban areas is crucial for smart cities. Even though several frameworks have been proposed in this task, there is no prior work focusing on the highly common, dense and size-variant vehicles such as motorcycles. In this paper, we propose a novel framework for vehicle motion counting with adaptive label-independent tracking and counting modules that processes 12 frames per second. Our framework adapts hyperparameters for multi-vehicle tracking and properly works in complex traffic conditions, especially invariant to camera perspectives. We achieved the competitive results in terms of root-mean-square error and runtime performance. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
Author Keywords	Object detection; Object tracking; Vehicle motion counting

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