Abnormal event detection in crowded scenarios

This paper proposes an autonomous video surveillance system which analyzes footages of extremely crowded scenes and detects abnormal events in the context of that particular scene. The model analyzes the local spatial-temporal motion pattern and detects abnormal motion variations and sudden changes and it can be divided into two major parts, selecting a set of Points of Interest (POI) from given frames and tracking them across multiple frames and dividing the input video frame in a number of cubes and track the motion patterns in each of the cubes for spatial-temporal statistical deviations. To evaluate the performance of proposed model we utilize several datasets and compare the acquired results of the proposed model with various state-of-the art models. Experimental results demonstrate that the proposed model outperforms the other models by exhibiting an average of 96.12% accuracy using Convolutional Neural Network.