Wang, Tao; He, Xuming; Barnes, Nick
In this paper, we propose a novel extension to the Class-specific Hough Forest (CHF) framework for object detection and localization. Our approach utilizes depth information during training to build a more discriminative codebook which simultaneously encodes features from the object and the surrounding context. In particular, we augment the CHF with contextual image patches, and design a series of depth-aware uncertainty measures for the binary tests used in CHF training. The new splitting...[Show more]
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