Detection in Crowded Scenes: One Proposal, Multiple Predictions

Abstract

We propose a simple yet effective proposal-based object detector, aiming at detecting highly-overlapped instances in crowded scenes. The key of our approach is to let each proposal predict a set of correlated instances rather than a single one in previous proposal-based frameworks. Equipped with new techniques such as EMD Loss and Set NMS, our detector can effectively handle the difficulty of detecting highly overlapped objects. On a FPN-Res50 baseline, our detector can obtain 4.9\% AP gains on challenging CrowdHuman dataset and 1.0\% ${\text{MR}}^{-2}$ improvements on CityPersons dataset, without bells and whistles. Moreover, on less crowed datasets like COCO, our approach can still achieve moderate improvement, suggesting the proposed method is robust to crowdedness. Code and pre-trained models will be released at https://github.com/megvii-model/CrowdDetection.

arXiv commentCVPR 2020 Oral

References in corpus (14)

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• YOLOv3: An Incremental Improvement
• R-FCN: Object Detection via Region-based Fully Convolutional Networks
• Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
• DSSD : Deconvolutional Single Shot Detector
• Focal Loss for Dense Object Detection
• CrowdHuman: A Benchmark for Detecting Human in a Crowd
• Scalable, High-Quality Object Detection
• Cascade R-CNN: High Quality Object Detection and Instance Segmentation
• MetaAnchor: Learning to Detect Objects with Customized Anchors
• Relation Networks for Object Detection
• SNIPER: Efficient Multi-Scale Training
• Sequential Context Encoding for Duplicate Removal
• PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes

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