Human Detection in Video Surveillance System

Object detection is a crucial part in today’s video surveillance systems. Many methods have evolved over the years that include Background Subtraction at the pinnacle. Background subtraction is a technique in which the video is segmented in multiple frames. A base frame called as “Background” is used to subtract another frame from it to detect “Foreground”. Motion–based and shape-based algorithms boost the Background subtraction method. The multiple objects detection technique used in surveillance system uses Support Vector Machine (SVM) to detect and classify the different objects. In this project, study proposes a novel object detection and its classification using Support Vector Machine (SVM) which is used to differentiate objects according to the set of points on the objects. The algorithm then aims at the classification of these key-points, namely at discriminating between the points which belongs to objects and all the others, by means of a Support Vector Machine (SVM) classifier. At the end of the procedure, the objects present inside the scene are identified by analyzing at the key-points previously classified as specific object points. It begins with a feature extraction process from which a set of consistent key-points is identified. Being able to identify specific objects or a particular class of objects in an image can provide several advantages and can open the door to the development of various interesting applications. DOI: 10.17762/ijritcc2321-8169.16048