Audio-Visual Co-Training for Vehicle Classification

In this paper, we introduce a fully autonomous vehicleclassification system that continuously learns from largeamounts of unlabeled data. For that purpose, we proposea novel on-line co-training method based on visual andacoustic information. Our system does not need complicatedmicrophone arrays or video calibration and automaticallyadapts to specific traffic scenes. These specialized detectorsare more accurate and more compact than generalclassifiers, which allows for light-weight usage in low-costand portable embedded systems. Hence, we implementedour system on an off-the-shelf embedded platform. In the experimentalpart, we show that the proposed method is ableto cover the desired task and outperforms single-cue systems.Furthermore, our co-training framework minimizesthe labeling effort without degrading the overall system performance.