Tracking-Based Semi-Supervised Learning

In this paper, we consider a semi-supervised approach to the problem of track classification in dense 3D range data. This problem involves the classification of objects that have been segmented and tracked without the use of a class-specific tracker. We propose a method based on the EM 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 LIDAR data collected from natural street scenes. When given only three handlabeled 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. Finally, we show that a simple algorithmic speedup based on incrementally updating a boosting classifier can reduce learning time by a factor of three.