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Deformable part models (DPM) already proved great performance for objects detection and many extensions have already published in literature. DPMs generally have high performance, though they dramatically fail to detect objects in challenging environments such as night time. This paper proposes a method based on the idea of latent parts to optimize the structure of objects in deformable part models...
Occlusion handling is crucial for developing ADAS in urban environment. Deformable Part Models (DPM) have already demonstrated state of art results in object detection. However, they fail to handle the occlusion due to inaccurate scores for part detectors when some object parts are occluded and not visible. To handle the imperfectness of part detectors in occlusion, we propose a two layers classifier...
Introduction of new local and semi-local features has played an important role in advancing the performance of object recognitions. Deformable part models prepare elegant framework for representing object categories and both efficient and accurate, achieving state-of the-art results. In this paper, We consider the problem of training a part-based model with variable size from images labeled only with...
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