Combining sparse appearance and Bayesian inference models for object tracking

In this paper, we present a method that combines a sparse appearance model into the Bayesian inference framework for tracking pedestrians in video sequences captured by a fixed camera. We formulate sparse appearance model as a linear combination of a set of 4D smoothed colour histograms for each pedestrian. These colour histograms are computed for all detection windows with different confidence values from human detector proposed by Dalal and Triggs. Object tracking is carried out using the Bayesian inference method. For occlusion handling, we integrate the Kalman filter to get the potential region containing target's observation and then use maximum a posteriori estimation to get the most likely observation. We test our tracking method on the benchmark video datasets. Our experimental results show that our tracking method outperforms one without using occlusion handling technique and can handle partial occlusion and false negative errors from human detector.