Event-driven Dynamic Platform Selection for Power-aware Real-time Anomaly Detection in Video

Blair, Calum G.; Robertson, Neil

doi:10.5220/0004737400540063

Proceedings of the 9th International Conference on Computer Vision Theory and Applications 2014

DOI: 10.5220/0004737400540063

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Event-driven Dynamic Platform Selection for Power-aware Real-time Anomaly Detection in Video

Calum G. Blair

Neil Robertson

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2021

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Good Practices and A Strong Baseline for Traffic Anomaly Detection

Zhao¹,

Wu²,

He³

et al. 2021

Preprint

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The detection of traffic anomalies is a critical component of the intelligent city transportation management system. Previous works have proposed a variety of notable insights and taken a step forward in this field, however, dealing with the complex traffic environment remains a challenge. Moreover, the lack of high-quality data and the complexity of the traffic scene, motivate us to study this problem from a handcrafted perspective. In this paper, we propose a straightforward and efficient framework that includes pre-processing, a dynamic track module, and post-processing. With video stabilization, background modeling, and vehicle detection, the pro-processing phase aims to generate candidate anomalies. The dynamic tracking module seeks and locates the start time of anomalies by utilizing vehicle motion patterns and spatiotemporal status. Finally, we use the postprocessing to fine-tune the temporal boundary of anomalies. Not surprisingly, our proposed framework was ranked 1 st in the NVIDIA AI CITY 2021 leaderboard for traffic anomaly detection. The code is available at: https: //github.com/Endeavour10020/AICity2021-Anomaly-Detection.

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Good Practices and A Strong Baseline for Traffic Anomaly Detection

Zhao¹,

Wu²,

He³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

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Event-driven Dynamic Platform Selection for Power-aware Real-time Anomaly Detection in Video

Cited by 1 publication

References 9 publications

Good Practices and A Strong Baseline for Traffic Anomaly Detection

Good Practices and A Strong Baseline for Traffic Anomaly Detection

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