Bi-box Regression for Pedestrian Detection and Occlusion Estimation

By Chunluan Zhou, Junsong Yuan. 2018

206 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Occluded Video Instance Segmentation: A Benchmark (2021, background): “[69] regresses two bounding boxes for each object to localize the full body and visible part of a pedestrian respectively.”
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].”
KGSNet: Key-Point-Guided Super-Resolution Network for Pedestrian Detection in the Wild (2020, methodology): “…heavy evaluation protocols, we compare our proposed KGSNet approach with some state-of-the-art methods: DeepParts [28], F-DNN+SS [20], FasterRCNN+ATT [40], CompACT-Deep [78], SA-FastRCNN [61], RPN+BF [22], MS-CNN [79], GDFL [39], TLL-TFA [41], SDS-RCNN [19], PDOE [38], RepLoss [25] and ALFNet [63].”
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].”
Adaptive NMS: Refining Pedestrian Detection in a Crowd (2019, background): “A common framework [28, 40, 51, 7, 27, 52] for occlusion handling is to learn a series of part detectors and integrate the results to localize occluded pedestrians.”
High-Level Semantic Feature Detection: A New Perspective for Pedestrian Detection (2019, background): “Bi-Box [53] proposes an auxiliary subnetwork to predict the visible part of a pedestrian instance.”
M3D-RPN: Monocular 3D Region Proposal Network for Object Detection (2019, background): “2D Detection: Many recent works have addressed 2D detection in both generic domain [17, 20, 22, 25, 28] and in urban scenes [2, 3, 23, 29, 38, 41].”
Temporal-Context Enhanced Detection of Heavily Occluded Pedestrians (2020, unknown): “5 Bi-Box [12] 16.”
Pedestrian Detection: The Elephant In The Room (2020, background): “After the initial works, Faster RCNN [30] became most popular framework with wide range of literature deploying it for pedestrian detection [44,42,6,5,25].”
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.”
Detection in Crowded Scenes: One Proposal, Multiple Predictions (2020, background): “There are other approaches on crowded detection, for example, part-based detectors [44, 38, 46, 3], which is mainly used in detecting special instances such as pedestrian.”