Audio-Visual Co-Training for Vehicle Classification

In this paper, we introduce a fully autonomous vehicle classification system that continuously learns from largeamounts of unlabeled data. For that purpose, we proposea novel on-line co-training method based on visual and acoustic information. Our system does not need complicated microphone arrays or video calibration and automatically adapts to specific traffic scenes. These specialized detectors are more accurate and more compact than general classifiers, which allows for light-weight usage in low-cost and portable embedded systems. Hence, we implemented our system on an off-the-shelf embedded platform. In the experimental part, we show that the proposed method is able to cover the desired task and outperforms single-cue systems. Furthermore, our co-training framework minimizes the labeling effort without degrading the overall system performance.