Paper-01 | Reg. No.:20141201|DOI:V3I3P01
Real time object Tracking Using Tracking Learning Detection
Rohan Gopale, Manoj Deshpande,A.C.Patil College of Engineering,Kharghar, Navi Mumbai.
In this paper we investigate long-term tracking of objects in a video stream. The object is defined by its location and extent in a single frame. In every frame that follows, the task is to determine the object’s location and extent or indicate that the object is not present. We propose a tracking framework (TLD) that explicitly focuses on long-term tracking task into tracking, learning, and detection. The tracker follows the object from frame to frame. The detector localizes all appearances that have been observed so far and corrects the tracker if necessary. Learning observes the performance of both tracker and detector, estimates detector’s errors, and generates training examples to avoid these errors in the future. A novel learning method (P-N learning) which estimates the errors by a pair of “experts”: (i) P-expert estimates missed detections, and (ii) N-expert estimates false alarms. The learning process is modelled as a discrete dynamical system and the conditions under which the learning guarantees improvement are found.