Multiple Anomalous Activity Detection in Videos

Due to exponential increase in crime rate, surveillance systems are being put up in malls, stations, schools, airports etc. With the videos being captured 24x7 from these cameras, it is difficult to manually monitor them to detect suspicious activities. So, there is a great demand for intelligent surveillance system. The proposed work automatically detects multiple anomalous activities in videos. The proposed framework includes three main steps: moving object detection, object tracking and behavior understanding for activity recognition. By using feature extraction process key features (speed, direction, centroid and dimensions) are identified. These features helps to track object in video frames. Problem domain knowledge rules helps to distinguish activities and dominant behavior of activities shows whether particular activity belongs to normal activity class or anomalous class. It has been experimentally proven that the proposed framework is capable of detecting multiple anomalous activities successfully with detection accuracy upto 90%. (C) 2018 The Authors. Published by Elsevier B.V.