{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T12:08:26Z","timestamp":1753272506769,"version":"3.38.0"},"reference-count":29,"publisher":"SAGE Publications","issue":"7","license":[{"start":{"date-parts":[[2012,5,30]],"date-time":"2012-05-30T00:00:00Z","timestamp":1338336000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["The International Journal of Robotics Research"],"published-print":{"date-parts":[[2012,6]]},"abstract":"<jats:p> We consider a semi-supervised approach to the problem of track classification in dense three-dimensional range data. This problem involves the classification of objects that have been segmented and tracked without the use of a class-specific tracker. This paper is an extended version of our previous work. <\/jats:p><jats:p> We propose a method based on the expectation\u2013maximization algorithm: iteratively (1) train a classifier, and (2) extract useful training examples from unlabeled data by exploiting tracking information. We evaluate our method on a large multiclass problem in dense range data collected from natural street scenes. <\/jats:p><jats:p> When given only three hand-labeled training tracks of each object class, the final accuracy of the semi-supervised algorithm is comparable to that of the fully supervised equivalent which uses two orders of magnitude more. Further, we show experimentally that the accuracy of a classifier considered as a function of human labeling effort can be substantially improved using this method. Finally, we show that a simple algorithmic speedup based on incrementally updating a boosting classifier can reduce learning time by a factor of three. <\/jats:p>","DOI":"10.1177\/0278364912442751","type":"journal-article","created":{"date-parts":[[2012,5,30]],"date-time":"2012-05-30T08:43:54Z","timestamp":1338367434000},"page":"804-818","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":43,"title":["Tracking-based semi-supervised learning"],"prefix":"10.1177","volume":"31","author":[{"given":"Alex","family":"Teichman","sequence":"first","affiliation":[{"name":"Department of Computer Science, Stanford University, USA"}]},{"given":"Sebastian","family":"Thrun","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Stanford University, 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