Multiple pedestrian tracking from monocular videos in an interacting multiple model framework

Publication Type:
Journal Article
IEEE Transactions on Image Processing, 2018, 27 (3), pp. 1361 - 1375
Issue Date:
Filename Description Size
08141418.pdfPublished Version7.7 MB
Adobe PDF
Full metadata record
© 2017 IEEE. 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.
Please use this identifier to cite or link to this item: