Robust Object Tracking in Infrared Video via Particle Filters

Abstract

In this paper we investigate the effectiveness of particle filters for object tracking in infrared videos. Once the user identifies the target object to be followed in position and size, its most representative feature points are obtained by means of the SURF algorithm. A particle filter is initialized with these feature points, and the location of the object within the video frames is determined by the average value of the particles that have a greater similarity with the target. Our aim is to make possible unnupervised object tracking in unmanned night flights. Two different field tests were carried out to study the filter behaviour in comparison with previously used methods in the bibliography. The first one was tracking an unmanned aerial vehicle (UAV) in the open. The second one was to identify a heliport in a noisy infrared zenithal video take. In the first test, the UAV was followed by another positioning system simultaneously, thus allowing the comparison of both systems, and the evaluation in the improvement introduced by the particle algorithm.