Tracking-Learning-Detection

Kalal, Z, Matas, J and Mikolajczyk, K (2012) Tracking-Learning-Detection IEEE Transactions on Pattern Analysis and Machine Intelligence, 34 (7). pp. 1409-1422.

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Abstract

This paper investigates long-term tracking of unknown 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 novel tracking framework (TLD) that explicitly decomposes the 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. The learning estimates detector's errors and updates it to avoid these errors in the future. We study how to identify detector's errors and learn from them. We develop 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 modeled as a discrete dynamical system and the conditions under which the learning guarantees improvement are found. We describe our real-time implementation of the TLD framework and the P-N learning. We carry out an extensive quantitative evaluation which shows a significant improvement over state-of-the-art approaches.

Item Type:	Article
Divisions :	Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :	Kalal, Z, Matas, J and Mikolajczyk, K
Date :	2012
DOI :	10.1109/TPAMI.2011.239
Additional Information :	Copyright 2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Depositing User :	Symplectic Elements
Date Deposited :	12 Oct 2012 10:06
Last Modified :	06 Jul 2019 05:10
URI:	http://epubs.surrey.ac.uk/id/eprint/713800

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