Abnormal behavior detection of stationary objects in surveillance videos with visualization and classification

Anomaly detection in video systems has been popular over several years. It is still challenging to detect anomalies in a static object. To manage this objective, we focus on changes in the position of a stationary object in videos. In a normal scenario, the pixel values of the static object are fixed while in abnormal motion the fixed values change. We introduce a new concept to determine anomalies based on manual annotations in each video frame, only over a part of a static object in a frame such that it can be taken as a reference for the whole. Through color channel splitting we determine mask image, from which handcrafted features such as scratch area, perimeter, equivalent diameter and density are calculated. In the next step, we analyze frame-wise changes in feature values using a linear regression model, feature values are constant when the object remains stationary while there is a rise or fall in values when an object changes location. We classify feature values through anomaly scores and thresholds. In this model, we are evaluating our proposed framework on 12 real-time video datasets. Results are compared with existing techniques which are outperforming in terms of accuracy, mean square error and area under the curve.