{"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/depth-based-object-tracking-using-a-robust","title":"Depth-Based Object Tracking Using a Robust Gaussian Filter","arxiv_id":"1602.06157","date":"2016-02-19","proceeding":null,"authors":["Jan Issac","Manuel Wüthrich","Cristina Garcia Cifuentes","Jeannette Bohg","Sebastian Trimpe","Stefan Schaal"],"abstract":"We consider the problem of model-based 3D-tracking of objects given dense\ndepth images as input. Two difficulties preclude the application of a standard\nGaussian filter to this problem. First of all, depth sensors are characterized\nby fat-tailed measurement noise. To address this issue, we show how a recently\npublished robustification method for Gaussian filters can be applied to the\nproblem at hand. Thereby, we avoid using heuristic outlier detection methods\nthat simply reject measurements if they do not match the model. Secondly, the\ncomputational cost of the standard Gaussian filter is prohibitive due to the\nhigh-dimensional measurement, i.e. the depth image. To address this problem, we\npropose an approximation to reduce the computational complexity of the filter.\nIn quantitative experiments on real data we show how our method clearly\noutperforms the standard Gaussian filter. Furthermore, we compare its\nperformance to a particle-filter-based tracking method, and observe comparable\ncomputational efficiency and improved accuracy and smoothness of the estimates.","url_abs":"http://arxiv.org/abs/1602.06157v1","url_pdf":"http://arxiv.org/pdf/1602.06157v1.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":"depth-based-object-tracking-using-a-robust","repo_url":"https://github.com/bayesian-object-tracking/dbot","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-tracking","task_name":"Object Tracking"},{"task_slug":"outlier-detection","task_name":"Outlier Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1602.06157","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}