{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ps-rcnn-detecting-secondary-human-instances","title":"PS-RCNN: Detecting Secondary Human Instances in a Crowd via Primary Object Suppression","arxiv_id":"2003.07080","date":"2020-03-16","proceeding":null,"authors":["Zheng Ge","Zequn Jie","Xin Huang","Rong Xu","Osamu Yoshie"],"abstract":"Detecting human bodies in highly crowded scenes is a challenging problem. Two main reasons result in such a problem: 1). weak visual cues of heavily occluded instances can hardly provide sufficient information for accurate detection; 2). heavily occluded instances are easier to be suppressed by Non-Maximum-Suppression (NMS). To address these two issues, we introduce a variant of two-stage detectors called PS-RCNN. PS-RCNN first detects slightly/none occluded objects by an R-CNN module (referred as P-RCNN), and then suppress the detected instances by human-shaped masks so that the features of heavily occluded instances can stand out. After that, PS-RCNN utilizes another R-CNN module specialized in heavily occluded human detection (referred as S-RCNN) to detect the rest missed objects by P-RCNN. Final results are the ensemble of the outputs from these two R-CNNs. Moreover, we introduce a High Resolution RoI Align (HRRA) module to retain as much of fine-grained features of visible parts of the heavily occluded humans as possible. Our PS-RCNN significantly improves recall and AP by 4.49% and 2.92% respectively on CrowdHuman, compared to the baseline. Similar improvements on Widerperson are also achieved by the PS-RCNN.","url_abs":"https://arxiv.org/abs/2003.07080v1","url_pdf":"https://arxiv.org/pdf/2003.07080v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"human-detection","task_name":"Human Detection"},{"task_slug":"object-detection","task_name":"Object Detection"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"r-cnn","method_name":"R-CNN"},{"method_slug":"svm","method_name":"SVM"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/object-detection-on-crowdhuman-full-body","task":"Object Detection","dataset":"CrowdHuman (full body)","model":"PS-RCNN (Faster RCNN, ResNet50, COCO Instance Masks","rank_in_archive_order":15,"of":19,"metrics":{"AP":"87.94"},"uses_additional_data":false},{"leaderboard":"/sota/object-detection-on-crowdhuman-full-body","task":"Object Detection","dataset":"CrowdHuman (full body)","model":"PS-RCNN (Faster RCNN, ResNet50)","rank_in_archive_order":16,"of":19,"metrics":{"AP":"86.05"},"uses_additional_data":false},{"leaderboard":"/sota/object-detection-on-widerperson","task":"Object Detection","dataset":"WiderPerson","model":"PS-RCNN (Faster RCNN, ResNet50)","rank_in_archive_order":2,"of":4,"metrics":{"AP":"89.96"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2003.07080","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}