Multi-Target Tracking by Online Learning of Non-Linear Motion Patterns and Robust Appearance Models
Abstract
We describe an online approach to learn non-linear motion patterns and robust appearance models for multi-target tracking in a tracklet association framework. Unlike most previous approaches that use linear motion methods only, we online build a non-linear motion map to better explain direction changes and produce more robust motion affinities between tracklets. Moreover, based on the incremental learned entry/exit map, a multiple instance learning method is devised to produce strong appearance models for tracking; positive sample pairs are collected from different track-lets so that training samples have high diversity. Finally, using online learned moving groups, a tracklet completion process is introduced to deal with tracklets not reaching entry/exit points. We evaluate our approach on three public data sets, and show significant improvements compared with state-of-art methods.
Cite
Text
Yang and Nevatia. "Multi-Target Tracking by Online Learning of Non-Linear Motion Patterns and Robust Appearance Models." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2012. doi:10.1109/CVPR.2012.6247892Markdown
[Yang and Nevatia. "Multi-Target Tracking by Online Learning of Non-Linear Motion Patterns and Robust Appearance Models." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2012.](https://mlanthology.org/cvpr/2012/yang2012cvpr-multi/) doi:10.1109/CVPR.2012.6247892BibTeX
@inproceedings{yang2012cvpr-multi,
title = {{Multi-Target Tracking by Online Learning of Non-Linear Motion Patterns and Robust Appearance Models}},
author = {Yang, Bo and Nevatia, Ram},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2012},
pages = {1918-1925},
doi = {10.1109/CVPR.2012.6247892},
url = {https://mlanthology.org/cvpr/2012/yang2012cvpr-multi/}
}