Real time object tracking via a mixture model

Abstract

Object tracking has been applied in many fields such as intelligent surveillance and computer vision. Although much progress has been made, there are still many puzzles which pose a huge challenge to object tracking. Currently, the problems are mainly caused by appearance model as well as real-time performance. A novel method was been proposed in this paper to handle both of these problems. Locally dense contexts feature and image information (i.e. the relationship between the object and its surrounding regions) are combined in a Bayes framework. Then the tracking problem can be seen as a prediction question which need to compute the posterior probability. Both scale variations and temple updating are considered in the proposed algorithm to assure the effectiveness. To make the algorithm runs in a real time system, a Fourier Transform (FT) is used when solving the Bayes equation. Therefore, the MMOT (Mixture model for object tracking) runs in real-time and performs better than state-of-the-art algorithms on some challenging image sequences in terms of accuracy, quickness and robustness.