@article{ref1,
title="Multiple pedestrian tracking from monocular videos in an interacting multiple model framework",
journal="IEEE transactions on image processing",
year="2018",
author="Jiang, Zhengqiang and Huynh, Du Q.",
volume="27",
number="3",
pages="1361-1375",
abstract="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 4-dimensional colour histogram as the appearance model for each pedestrian returned by a people detector that is based on the Histogram of Oriented Gradients (HOG) 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.<p /> <p>Language: en</p>",
language="en",
issn="1057-7149",
doi="10.1109/TIP.2017.2779856",
url="http://dx.doi.org/10.1109/TIP.2017.2779856"
}