Multiple Pedestrian Tracking From Monocular Videos in an Interacting Multiple Model Framework

We present a multiple pedestrian tracking method for monocular videos captured by a fixed camera in an interacting multiple model (IMM) framework. Our tracking method involves multiple IMM trackers running in parallel, which are tied together by a robust data association component. We investigate two data association strategies which take into account both the target appearance and motion errors. We use a 4D color histogram as the appearance model for each pedestrian returned by a people detector that is based on the histogram of oriented gradients features. Short-term occlusion problems and false negative errors from the detector are dealt with using a sliding window of video frames, where tracking persists in the absence of observations. Our method has been evaluated, and compared both qualitatively and quantitatively with four state-of-the-art visual tracking methods using benchmark video databases. The experiments demonstrate that, on average, our tracking method outperforms these four methods.