DETECTION OF SUSPICIOUS ACTIVITIES IN PUBLIC AREAS USING STAGED MATCHING TECHNIQUE

Abstract—Detection of suspicious activities of human crowd scenes in public areas using video surveillance has attracted an increasing level of care. A framework that contains video data receives from a fixed color camera installed at a particular location. The noise from video frames is removed by using Gaussian filtering with color and gamma correction. The foreground blob is extracted from video frames using background subtraction method. The framework obtains 3-D object level information by detecting and tracking persons and luggage in the scene. Using staged matching technique, the detection of merging and splitting in occlusion. The actions of public are identified and clustered in a crowd scene by using an adjacency matrix-based clustering and support vector machine. The features are extracted from the frames using Gabor algorithm and histogram of gradient. To predict the behaviors of human crowd based on the model and then detect if any anomalies of human crowd present in the scene that is relevant to security in public areas. The experimental results are to demonstrate the outstanding performance by using extensive dataset, fast object tracking, low computational complexity and effective in detecting anomalous events for uncontrolled environment of surveillance videos. Keywords—Crowd behavior, suspicious activities, anomalous events, adjacent matrix-based clustering, support vector machine. I