iccv iccv2013 iccv2013-269 iccv2013-269-reference knowledge-graph by maker-knowledge-mining
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Author: Bo Li, Wenze Hu, Tianfu Wu, Song-Chun Zhu
Abstract: Occlusion presents a challenge for detecting objects in real world applications. To address this issue, this paper models object occlusion with an AND-OR structure which (i) represents occlusion at semantic part level, and (ii) captures the regularities of different occlusion configurations (i.e., the different combinations of object part visibilities). This paper focuses on car detection on street. Since annotating part occlusion on real images is time-consuming and error-prone, we propose to learn the the AND-OR structure automatically using synthetic images of CAD models placed at different relative positions. The model parameters are learned from real images under the latent structural SVM (LSSVM) framework. In inference, an efficient dynamic programming (DP) algorithm is utilized. In experiments, we test our method on both car detection and car view estimation. Experimental results show that (i) Our CAD simulation strategy is capable of generating occlusion patterns for real scenarios, (ii) The proposed AND-OR structure model is effective for modeling occlusions, which outperforms the deformable part-based model (DPM) [6, 10] in car detec- , tion on both our self-collected streetparking dataset and the Pascal VOC 2007 car dataset [4], (iii) The learned model is on-par with the state-of-the-art methods on car view estimation tested on two public datasets.
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