Performance Improvement of Moving Object Recognition and Tracking Algorithm Using Parallel Processing of SURF and Optical Flow

The paper proposes a way of parallel processing of SURF and Optical Flow for moving object recognition and tracking. The object recognition and tracking is one of the most important task in computer vision, however, disadvantage are many operations cause processing speed slower so that it can't do real-time object recognition and tracking. The proposed method uses a typical way of feature extraction SURF and moving object Optical Flow for reduce disadvantage and real-time moving object recognition and tracking, and parallel processing techniques for speed improvement.