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arXiv · 1808.10044

AAD: Adaptive Anomaly Detection through traffic surveillance videos

Mohammmad Farhadi Bajestani·Seyed Soroush Heidari Rahmat Abadi·Seyed Mostafa Derakhshandeh Fard·Roozbeh Khodadadeh
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Abstract

Anomaly detection through video analysis is of great importance to detect any anomalous vehicle/human behavior at a traffic intersection. While most existing works use neural networks and conventional machine learning methods based on provided dataset, we will use object recognition (Faster R-CNN) to identify objects labels and their corresponding location in the video scene as the first step to implement anomaly detection. Then, the optical flow will be utilized to identify adaptive traffic flows in each region of the frame. Basically, we propose an alternative method for unusual activity detection using an adaptive anomaly detection framework. Compared to the baseline method described in the reference paper, our method is more efficient and yields the comparable accuracy.

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Mohammmad Farhadi Bajestani, Seyed Soroush Heidari Rahmat Abadi, Seyed Mostafa Derakhshandeh Fard, Roozbeh Khodadadeh. 2018-08-29. AAD: Adaptive Anomaly Detection through traffic surveillance videos. https://arxiv.org/abs/1808.10044

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