Author : J.Jasmine Anitha and S.M.Deepa

Visual object tracking for surveillance applications is an important task in computer vision. Tracking of object is a matching problem. One main difficulty in object tracking is to choose suitable features and models for recognizing and tracking the target. SURF (Speeded Up Robust Features) algorithm is used here for continuous image recognition and tracking in video. The SURF feature descriptor operates by reducing the search space of possible interest points inside of the scale space image pyramid. SURF adds a lot of features to improve the speed in every step. The resulting tracked interest points are more repeatable and noise free. SURF is good at handling images with blurring and rotation. Corner detection is good for obtaining image features for object tracking and recognition. Interest points in an image are located using corner detector. By using harris corner detection algorithm along SURF feature descriptor, tracking efficiency is improved.

Keywords: Video surveillance, object tracking, object recognition, motion estimation, interest points, edge orientation

Article published in International Journal of Current  Engineering  and Technology, Vol.4,No.2 (April- 2014)