Imperial College London


Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Reader in Computer Vision



+44 (0)20 7594 6220k.mikolajczyk




Electrical EngineeringSouth Kensington Campus






BibTex format

author = {Kalal, Z and Matas, J and Mikolajczyk, K},
doi = {10.1109/TPAMI.2011.239},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
pages = {1409--1422},
title = {Tracking-Learning-Detection},
url = {},
volume = {34},
year = {2012}

RIS format (EndNote, RefMan)

AB - 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.
AU - Kalal,Z
AU - Matas,J
AU - Mikolajczyk,K
DO - 10.1109/TPAMI.2011.239
EP - 1422
PY - 2012///
SN - 0162-8828
SP - 1409
TI - Tracking-Learning-Detection
T2 - IEEE Transactions on Pattern Analysis and Machine Intelligence
UR -
VL - 34
ER -