Markov Chain Monte Carlo Modular Ensemble Tracking
Recent years have been characterized by the overgrowth of video-surveillance systems and by automation of the processing they integrate. Object Tracking has become a recurrent problem in video-surveillance and is a very important domain in computer vision. It was recently approached using classification techniques and still more recently using boosting methods. We propose in this paper a new machine learning based strategy to build the observation model of tracking systems. The global observation function results of a linear combination of several simplest observation functions so-called modules (one per visual cue). Each module is built using a Adaboost-like algorithm, derived from the Ensemble Tracking Algorithm. The importance of each module is estimated using an original probabilistic sequential filtering framework with a joint state model composed by both the spatial object parameters and the importance parameters of the observation modules. Our system is tested on challenging sequences which prove its performance for tracking and scaling on fix and mobile cameras and we compare the robustness of our algorithm with the state of the art. (C) 2012 Elsevier B.V. All rights reserved.
object tracking classification boosting feature spaces particle filtering