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LRATD: a lightweight real-time abnormal trajectory detection approach for road traffic surveillance

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Abstract

Occasions such as stalled vehicles or crashes led by abnormal trajectories should be instantly identified and then dealt with quickly by the city traffic management system for the sake of road safety. However, a fast and accurate automatic detection system based on machine learning in general meets with great challenges from the shortage of recorded accident data, resulting in low detection accuracy. Many existing studies implement a two-level detection approach: stalled vehicles are detected at the stationary level, while abnormal trajectories are detected at the mobile level. This paper proposes a novel triple-layer framework to distribute these two levels to three parallel layers for maximum efficiency. A straightforward background extraction algorithm is applied at the beginning of this framework for motion-stationary distribution. Layer 1 implements a lightweight optical-flow-based feature extraction algorithm to convert the mobile visual features to learnable data. With a clustering algorithm that learns the common trajectories in an unsupervised manner, abnormal trajectories are detected in Layer 2. Simultaneously, in Layer 3, a custom-trained object detection algorithm is applied to detect the stall/crashed vehicles. The computational efficiency is improved and the detection accuracy is boosted. Experiments conducted on Nvidia AI City Challenge Dataset demonstrate the effectiveness of our LRATD (Lightweight Real-Time Abnormal Trajectory Detection framework) in terms of \(104\%\) gain in detection speed compared to the fastest entry, while achieving 0.935 S4-Score, only \(2.1\%\) less than the current state-of-the-art method. Overall, the performance of LRATD opens the possibility of its real-life application.

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Data Availability

The data used and evaluated in this study are available in Nvidia AI City Challenge [53] and the UC-DETRAC dataset [81]. Output examples of LRATD are included in the supplementary materials.

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Acknowledgements

This work was supported in part by National Key Research and Development Project No.2019YFC1511003, National Natural Science Foundation of China (NSFC) No.61803004, Aeronautical Science Foundation of China No.20161375002 through grants for our project.

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    Chun Zhang & Keyan Ren

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Zhang, C., Ren, K. LRATD: a lightweight real-time abnormal trajectory detection approach for road traffic surveillance. Neural Comput & Applic 34, 22417–22434 (2022). https://doi.org/10.1007/s00521-022-07626-2

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