Audio-Visual Co-Training for Vehicle Classification

Proceedings IEEE International Conference on Video and Signal-Based Surveillance

In this paper, we introduce a fully autonomous vehicle
classification system that continuously learns from large
amounts of unlabeled data. For that purpose, we propose
a 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.