Pedestrian Behavior Understanding and Prediction with Deep Neural Networks
Abstract
In this paper, a deep neural network (Behavior-CNN) is proposed to model pedestrian behaviors in crowded scenes, which has many applications in surveillance. A pedestrian behavior encoding scheme is designed to provide a general representation of walking paths, which can be used as the input and output of CNN. The proposed Behavior-CNN is trained with real-scene crowd data and then thoroughly investigated from multiple aspects, including the location map and location awareness property, semantic meanings of learned filters, and the influence of receptive fields on behavior modeling. Multiple applications, including walking path prediction, destination prediction, and tracking, demonstrate the effectiveness of Behavior-CNN on pedestrian behavior modeling.
Cite
Text
Yi et al. "Pedestrian Behavior Understanding and Prediction with Deep Neural Networks." European Conference on Computer Vision, 2016. doi:10.1007/978-3-319-46448-0_16Markdown
[Yi et al. "Pedestrian Behavior Understanding and Prediction with Deep Neural Networks." European Conference on Computer Vision, 2016.](https://mlanthology.org/eccv/2016/yi2016eccv-pedestrian/) doi:10.1007/978-3-319-46448-0_16BibTeX
@inproceedings{yi2016eccv-pedestrian,
title = {{Pedestrian Behavior Understanding and Prediction with Deep Neural Networks}},
author = {Yi, Shuai and Li, Hongsheng and Wang, Xiaogang},
booktitle = {European Conference on Computer Vision},
year = {2016},
pages = {263-279},
doi = {10.1007/978-3-319-46448-0_16},
url = {https://mlanthology.org/eccv/2016/yi2016eccv-pedestrian/}
}