@inproceedings{f561d3956d154723a856ddd862c589a7,
title = "Part-based data association for visual tracking",
abstract = "We present a method that integrates a part-based sparse appearance model in a Bayesian inference framework for tracking targets in video sequences. We formulate the sparse appearance model as a set of smoothed colour histograms corresponding to the object windows detected by the Deformable Part Model (DPM) detector. The data association of each body part between frames is solved based on the position constraint, appearance coherence, and motion consistency. To deal with missing and noisy observations, the part detection window in the following frame is also predicted using an interacting multiple model (IMM) tracker. We have tested our tracking method on all the video sequences that involve people in upright poses from the TB-50 and TB-100 benchmark videos datasets. Our experimental results show that our tracking method outperforms six state-of-the-art tracking techniques. {\textcopyright} 2017 IEEE.",
author = "Zhengqiang Jiang and Huynh, {Du Q.} and Jian Zhang and Qiang Wu",
year = "2017",
month = dec,
day = "19",
doi = "10.1109/DICTA.2017.8227474",
language = "English",
isbn = "9781538628393",
series = "DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
pages = "1--8",
editor = "Yi Guo and Manzur Murshed and Zhiyong Wang and Feng, {David Dagan} and Hongdong Li and Cai, {Weidong Tom} and Junbin Gao",
booktitle = "2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)",
address = "United States",
note = "2017 International Conference on Digital Image Computing: Techniques and Applications, DICTA ; Conference date: 29-11-2017 Through 01-12-2017",
}