Semi-supervised learning for robust car windshield tracking and monitoring in live traffic videos

This paper deals with the problem of car-windshield tracking in live traffic video. To avoid a comprehensive labeled dataset that covers most appearance variations, we aim to appropriately involve unlabeled examples and efficiently update the discriminative model in an online semi-supervised setting. Our approach follows the state-of-the- art “learning by detection” approach, yet different from it in the following aspects. First, instead of assigning hard labels to new added examples, we leave them unlabeled. Second, we focus on exploring the intrinsic manifold structure of data marginal distribution and studying its role in kernel function optimization. The proposed online semi-supervised learning framework involves a 3D mean-shift optimization for windshield localization and is followed by a block-based decision for co-driver detection. The experimental results demonstrate the effectiveness of the proposed method.