Multiple Object Tracking using Support Vector Machine
This paper presents an accurate and flexible method for robust recognition and tracking of multiple objects in video sequence. Object tracking is the process of separating the moving object from the video sequences. Tracking is essentially a matching problem in object tracking. In order to avoid this matching problem, object recognition is done on the tracked object. Background separation algorithm separate moving object from the background based on white and black pixels. Support Vector Machines classifier is used to recognize the tracked object. SVM classifier are supervised learning that associates with machine learning algorithm that analyse and recognize the data used for classification. SVM uses Kalman filter which makes the system more robust by tracking and reduce the noise introduced by inaccurate detections.
