Abstract
The main task of multi-object tracking is to associate targets in diverse images by detected information from each frame of a given image sequence. For the scenario of highway video surveillance, the equivalent research issue is vehicles tracking, which is necessary and fundamental for traffic statistics, abnormal events detection, traffic control et al. In this paper, a simplified and efficient multi-object tracking strategy is proposed. Based on the position and intersection-over-union (IOU) of the moving object, the color feature is derived, and unscented Kalman filter is involved to revise targets’ positions. This innovative tracking method can effectively solve the problem of target occlusion and loss. The simplicity and efficiency make this algorithm applicable for the perspective of real-time system. In this paper, highway video recordings are explored as data repository for experiments. The results show that our method outperforms on the issue of vehicle tracking.
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This research is supported by the National Key R&D Program of China under Grant No. 2018YFB1003404.
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Guan, H., Liu, H., Yu, M., Zhao, Z. (2019). Research and Implementation of Vehicle Tracking Algorithm Based on Multi-Feature Fusion. In: Ni, W., Wang, X., Song, W., Li, Y. (eds) Web Information Systems and Applications. WISA 2019. Lecture Notes in Computer Science(), vol 11817. Springer, Cham. https://doi.org/10.1007/978-3-030-30952-7_8
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