Abstract
We present a novel max-margin Hough transform with latent structure for joint object detection and pose estimation. Our method addresses the large appearance and shape variation of objects in multiple poses by integrating three key components: First, we propose a more robust appearance model by designing a patch dictionary with complementary features; In addition, we use a group of latent components to explicitly incorporate feature selection and pooling into the Hough-based object models; Furthermore, we adopt a multiple instance learning approach to handle the lack of correspondence among training instances with noisy bounding-box labels. We design a unified objective and an efficient approximate inference that alternates the search between object location and pose space. We demonstrate the efficacy of our approach by achieving the state-of-the-art performance on two detection and two joint estimation datasets.
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This work is supported by National Natural Science Foundation of China (Project NO: 61503168).
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Li, H., He, X., Barnes, N., Wang, M. (2016). Learning Hough Transform with Latent Structures for Joint Object Detection and Pose Estimation. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9517. Springer, Cham. https://doi.org/10.1007/978-3-319-27674-8_11
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DOI: https://doi.org/10.1007/978-3-319-27674-8_11
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