Abstract
Vehicle detection and vehicle viewpoint estimation are both crucial for assistive and autonomous driving systems. In this paper, we propose a soft discriminative mixture of viewpoint (SDMoV) models for joint vehicle detection and vehicle viewpoint estimation. The proposed SDMoV model is learned in two steps. First, a discriminative viewpoint-specific component model, which aims to maximize vehicle viewpoint classification accuracy, is learned for each cluster of vehicle images with similar viewpoint. Second, a new soft margin objective function, which aims to maximize vehicle detection accuracy, is designed to retrain these component models into a mixture of viewpoint models. The proposed SDMoV model is capable of detecting vehicles and estimating their viewpoints simultaneously. Experiments on three state-of-the-art datasets show that the proposed SDMoV model achieves superior accuracy for both vehicle detection and vehicle viewpoint estimation tasks.
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