Paper Info
Title
Attribute-Aware Pedestrian Detection In A Crowd
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.
Year
DOI
Venue
2021
10.1109/TMM.2020.3020691
IEEE TRANSACTIONS ON MULTIMEDIA
Keywords
DocType
Volume
Detectors, Semantics, Feature extraction, Proposals, Object detection, Task analysis, Training, Attribute-aware, non-maximum suppression (nms), pedestrian detection
Journal
23
ISSN
Citations 
PageRank 
1520-9210
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Jialiang Zhang19411.46
Lixiang Lin200.34
Jianke Zhu3170268.54
Yang Li4659125.00
Yun-chen Chen500.34
Yao Hu621616.71
Steven C. H. Hoi726817.70