ISSN 2348 – 9928 IJAICT Volume -1, Issue-1, May 2014 Doi:01.0401/ijaict.2014.01.27 Published Online 05 (05) 2014 © 2014 IJAICT (www.ijaict.com) Corresponding Author: Ms. K. Pavithradevi, Sri Guru Institute of Technology, Coimbatore, Tamilnadu , India 140 DETECTION OF SUSPICIOUS ACTIVITIES IN PUBLIC AREAS USING STAGED MATCHING TECHNIQUE Ms. K. Pavithradevi PG Scholar, Department of Computer Science and Engineering, SriGuru Institute of Technology, Coimbatore, Tamilnadu, India Ms. S. Aruljothi Assistant Professor, Department of Computer Science and Engineering, SriGuru Institute of Technology, Coimbatore, Tamilnadu, India 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. INTRODUCTION The activities of human crowd behavior using surveillance videos is an vital issue for public security, as it allows detection of both anomalies and abnormal in human crowd behavior being important surveillance applications. Anomalous behavior recognition and video understanding are core components in video surveillance system. The detection of changes in human crowd, behaviors and anomalies in imagery and video is a problem in machine vision. Lately there has been much effort to devise automated real time high accuracy video surveillance systems. This practice is almost witness in large public areas such as metro station and airport. The purpose of this paper is to identify the activities of behaviors, anomalous events and suspicious behavior of human crowd in public areas. The framework that processes raw video data receives from a fixed color camera at a particular location. The preprocessing stage is done to removes noise from video frame using Gaussian filtering with color correction and gamma correction to improve the quality of the image. The background subtraction method is used to subtract the background in each video frame and extract the foreground objects as blobs. The human crowd areas in video frames are notified after the extraction of foreground blobs. The blobs are extracted in foreground that as automatically finds human crowd and single areas. The clustered objects are obtained by background segmentation into semantic entities in the scenes. The clustered objects are separated by using adjacent matrix based clustering. The action and various anomalous events are detecting by using support vector machine. After that individual objects are completely modeling and tracking. A complete semantic based recognition that depends on object tracking has been visualized and extensively investigated. These objects are tracked by using particle filtering with color histogram, spatiogram and structural similarity index measure. The color objects are tracked in 2-D and classified as being either animate (people) or inanimate (object) in human crowd scene. These objects are modeled by using spatio spectral algorithm to estimate the pixel color and halt update of occlusion stage. The objects are matching with blob by comparing of color histogram and histogram intersection with threshold. The matched object and blobs are move to feature calculation. The occlusion of unmatched blob and objects are separated by using staged matching technique potential occlusion method to detect merges and splits. The unmatched blobs are processes into new object and recover the objects. The feature calculation is based on the histogram of gradient and gabor algorithm to form in the order of historical sequence by using recorded dataset and local features of human crowd scenes