Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd

By Shifeng Zhang, Longyin Wen, Xiao Bian, Zhen Lei, S. Li. 2018

446 citations recorded by the source corpus.

Pedestrian detection in crowded scenes is a challenging problem since the pedestrians often gather together and occlude each other. In this paper, we propose a new occlusion-aware R-CNN (OR-CNN) to improve the detection accuracy in the crowd. Specifically, we design a new aggregation loss to enforce proposals to be close and locate compactly to the corresponding objects. Meanwhile, we use a new part occlusion-aware region of interest (PORoI) pooling unit to replace the RoI pooling layer in order to integrate the prior structure information of human body with visibility prediction into the network to handle occlusion. Our detector is trained in an end-to-end fashion, which achieves state-of-the-art results on three pedestrian detection datasets, i.e., CityPersons, ETH, and INRIA, and performs on-pair with the state-of-the-arts on Caltech.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Occluded Video Instance Segmentation: A Benchmark (2021, methodology): “We also migrate three image-level detection methods to the CMaskTrack R-CNN model, including 1) the repulsion loss [76] which requires the predicted boxes to keep away from other ground-truth boxes; 2) the compact loss [92] which enforces proposals to be close and locate compactly to the correspondi”
Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers (2021, methodology): “For pedestrian detection with occlusion, bi-box regression is proposed in [64] for both full body and visible part estimation while repulsion loss [56] and aggregation loss [63] are designed to improve the detection accuracy.”
Adaptive NMS: Refining Pedestrian Detection in a Crowd (2019, methodology): “To address this problem, [44] and [49] propose additional penalties to produce more compact bounding boxes and thus become less sensitive to the threshold of NMS.”
Detection in Crowded Scenes: One Proposal, Multiple Predictions (2020, background): “For example, [43] proposes Aggregation Loss to enforce proposals to be close and locate compactly to the corresponding ground truth.”
Recent Advances in Deep Learning for Object Detection (2019, background): “Also to reduce the impact of large variance in scales, Li et al. [8] proposed Scale-aware For pedestrian detection, one of the most signiﬁcant challenges is to handle occlusion [216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 214, 226].”
A Survey of Deep Learning-Based Object Detection (2019, background): “[102] propose a newly designed occlusion-aware R-CNN (OR-CNN) to improve the detection accuracy in the crowd.”
High-Level Semantic Feature Detection: A New Perspective for Pedestrian Detection (2019, methodology): “4 (c) shows that CSP also performs very well for heavily occluded pedestrians, outperforming RepLoss [46] and OR-CNN [52] which are explicitly designed for occlusion cases.”
Occluded Prohibited Items Detection: An X-ray Security Inspection Benchmark and De-occlusion Attention Module (2020, background): “There are several works trying to solve occlusion problems in different scenarios, such as person re-identification [37, 42, 43], face recognition [6, 28, 38].”
Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge (2021, background): “[34, 41] propose new loss functions to enforce predicted box to locate compactly to the corresponding ground-truth objects while far from other objects.”
Temporal-Context Enhanced Detection of Heavily Occluded Pedestrians (2020, methodology): “To alleviate the occlusion issue, recent methods are designed by exploiting attention mechanism [29, 9], feature transformation [11] and part-based detection [22, 19, 13].”
KGSNet: Key-Point-Guided Super-Resolution Network for Pedestrian Detection in the Wild (2020, methodology): “We compare our proposed KGSNet with several state-of-the-art pedestrian detectors on the CityPersons validation set, including attention RCNN [40], adapted faster RCNN [24], TTL [41], repulsion loss [25], OR-RCNN [26], ALFNet [63], and Bi-box Reg [38].”
Bayesian Loss for Crowd Count Estimation With Point Supervision (2019, background): “In overcrowded scenes, clutters and severe occlusions make it unfeasible to detect every single person, despite the progresses in related ﬁelds [19, 10, 47, 17, 33, 43, 61, 50, 48, 34, 49, 55, 60, 52].”