Publication Type
Journal Article
Version
acceptedVersion
Publication Date
1-2021
Abstract
Pedestrian detection is an initial step to perform outdoor scene analysis, which plays an essential role in many real-world applications. Although having enjoyed the merits of deep learning frameworks from the generic object detectors, pedestrian detection is still a very challenging task due to heavy occlusions, and highly crowded group. Generally, the conventional detectors are unable to differentiate individuals from each other effectively under such a dense environment. To tackle this critical problem, we propose an attribute-aware pedestrian detector to explicitly model people's semantic attributes in a high-level feature detection fashion. Besides the typical semantic features, center position, target's scale, and offset, we introduce a pedestrian-oriented attribute feature to encode the high-level semantic differences among the crowd. Moreover, a novel attribute-feature-based Non-Maximum Suppression (NMS) is proposed to distinguish the person from a highly overlapped group by adaptively rejecting the false-positive results in a very crowd settings. Furthermore, an enhanced ground truth target is designed to alleviate the difficulties caused by the attribute configuration, and to ease the class imbalance issue during training. Finally, we evaluate our proposed attribute-aware pedestrian detector on three benchmark datasets including CityPerson, CrowdHuman, and EuroCityPerson, and achieves the state-of-the-art results.
Keywords
Attribute-aware, non-maximum suppression (nms), pedestrian detection
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Research Areas
Data Science and Engineering
Publication
IEEE Transactions on Multimedia
Volume
23
First Page
3085
Last Page
3097
ISSN
1520-9210
Identifier
10.1109/TMM.2020.3020691
Publisher
Institute of Electrical and Electronics Engineers
Citation
ZHANG, Jialiang; LIN, Lixiang; ZHU, Jianke; LI, Yang; CHEN, Yun-chen; HU, Yao; and HOI, Steven C. H..
Attribute-aware pedestrian detection in a crowd. (2021). IEEE Transactions on Multimedia. 23, 3085-3097.
Available at: https://ink.library.smu.edu.sg/sis_research/6967
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TMM.2020.3020691
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons