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 model. 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 suburban street scenes. When given only three hand-labeled training tracks of each object class, semi-supervised performance is comparable to that of the fully-supervised equivalent which uses thousands of hand-labeled training tracks. Further, when given additional unlabeled data, the semi-supervised method outperforms the supervised 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.