{"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/dpdnet-a-robust-people-detector-using-deep","title":"DPDnet: A Robust People Detector using Deep Learning with an Overhead Depth Camera","arxiv_id":"2006.01053","date":"2020-06-01","proceeding":null,"authors":["David Fuentes-Jimenez","Roberto Martin-Lopez","Cristina Losada-Gutierrez","David Casillas-Perez","Javier Macias-Guarasa","Daniel Pizarro","Carlos A. Luna"],"abstract":"In this paper we propose a method based on deep learning that detects multiple people from a single overhead depth image with high reliability. Our neural network, called DPDnet, is based on two fully-convolutional encoder-decoder neural blocks based on residual layers. The Main Block takes a depth image as input and generates a pixel-wise confidence map, where each detected person in the image is represented by a Gaussian-like distribution. The refinement block combines the depth image and the output from the main block, to refine the confidence map. Both blocks are simultaneously trained end-to-end using depth images and head position labels. The experimental work shows that DPDNet outperforms state-of-the-art methods, with accuracies greater than 99% in three different publicly available datasets, without retraining not fine-tuning. In addition, the computational complexity of our proposal is independent of the number of people in the scene and runs in real time using conventional GPUs.","url_abs":"https://arxiv.org/abs/2006.01053v1","url_pdf":"https://arxiv.org/pdf/2006.01053v1.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":[{"paper_slug":"dpdnet-a-robust-people-detector-using-deep","repo_url":"https://github.com/lehommee/DPDnet-A-robust-people-detector-using-deep-learning-with-an-overhead-depth-camera","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"dpdnet-a-robust-people-detector-using-deep","repo_url":"https://github.com/ShubhamShaswat/Overhead-Person-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"head-detection","task_name":"Head Detection"},{"task_slug":"human-detection","task_name":"Human Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}