Human Anomalous Activity Detection: Shape and Motion Approach in Crowded Scenes

Abstract

Detecting anomalous activities in crowded scenes is a very challenging task in computer vision. An enhanced video anomaly detection framework is proposed for frame-wise anomalous activity detection in crowded scenes that is based on both shape and motion based features. The Histogram of Oriented Gradients (HOG) is used to represent the shape based features of the video frames and for representing the motion, Histogram of Oriented Optical Flow (HOOF) is used. These features are modeled using two-class Support Vector Machines (SVM) to detect abnormal events in every frame. The proposed method is modeled with both normal and abnormal behaviors which are learnt from the training data and it is capable of detecting abnormal activities in a live surveillance video. To evaluate the performance of the proposed work, experiments are conducted on the standard benchmark UCSD data set and the results are compared with the HOOF feature bin values.