Identification of Vehicle Trajectory Anomalies on Streaming Video

Anikin, Igor V.; Mardanova, Aigul

doi:10.1007/978-3-030-95116-0_21

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Cited by 2 publications

References 17 publications

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Adaptive Trajectory Data Stream Clustering

Sunitha,

Kiran,

Nagendra

et al. 2024

Lecture Notes in Networks and Systems

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Adaptive Trajectory Data Stream Clustering

Sunitha,

Kiran,

Nagendra

et al. 2024

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Automated detection of vehicles with anomalous trajectories in traffic surveillance videos

Fernández-Rodríguez

García‐González

Benítez-Rochel

et al. 2023

ICA

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Video feeds from traffic cameras can be useful for many purposes, the most critical of which are related to monitoring road safety. Vehicle trajectory is a key element in dangerous behavior and traffic accidents. In this respect, it is crucial to detect those anomalous vehicle trajectories, that is, trajectories that depart from usual paths. In this work, a model is proposed to automatically address that by using video sequences from traffic cameras. The proposal detects vehicles frame by frame, tracks their trajectories across frames, estimates velocity vectors, and compares them to velocity vectors from other spatially adjacent trajectories. From the comparison of velocity vectors, trajectories that are very different (anomalous) from neighboring trajectories can be detected. In practical terms, this strategy can detect vehicles in wrong-way trajectories. Some components of the model are off-the-shelf, such as the detection provided by recent deep learning approaches; however, several different options are considered and analyzed for vehicle tracking. The performance of the system has been tested with a wide range of real and synthetic traffic videos.

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Identification of Vehicle Trajectory Anomalies on Streaming Video

Cited by 2 publications

References 17 publications

Adaptive Trajectory Data Stream Clustering

Adaptive Trajectory Data Stream Clustering

Automated detection of vehicles with anomalous trajectories in traffic surveillance videos

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