Abstract
People tracking is an interesting topic in computer vision. It has applications in industrial areas such as surveillance or human-machine interaction. Particle Filters is a common algorithm for people tracking; challenging situations occur when the target's motion is poorly modelled or with unexpected motions. In this paper, an alternative to address people tracking is presented. The proposed algorithm is based in particle filters, but instead of using a dynamical model, it uses background subtraction to predict future locations of particles. The algorithm is able to track people in omnidirectional sequences with a low frame rate (one or two frames per second). Our approach can tackle unexpected discontinuities and changes in the direction of the motion. The main goal of the paper is to track people from laboratories, but it has applications in surveillance, mainly in controlled environments.
Highlights
Omnidirectional Vision Systems are the topic of many research activities in recent years
The authors present the experiments with an omnidirectional sequence
The results of the proposed algorithm are compared with a standard particle filter
Summary
Omnidirectional Vision Systems are the topic of many research activities in recent years. People tracking is a major topic in computer vision. PF use system transitions to model the motion of the target. These transitions add flexibility in comparison to the Kalman filter. Despite there being lots of particle filter applications that succeed tracking targets, the modelling of dynamics represents a great challenge. In people tracking with low frame-rate sequences, it is very difficult to model significant random jumps of subjects. Haritaoglu et al (2000) modelled the variation of the background with a bimodal distribution build with order statistics of the pixel values over a training period. The background model represents each pixel with three values: minimum, maximum and the greater difference between consecutive frames over the training period. Given the minimum (M), maximum (N) and the greater differences between frames (D), the pixels x of an image I will be foreground if:
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