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Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model
Bo Li1, 2, Tianfu Wu2, and Song-Chun Zhu2
1Beijing Lab of Intelligent Information Technology, Beijing Institute of Technology, China
2Department of Statistics, University of California, Los Angeles, USA
boli86@bit.edu.cn
tfwu@stat.ucla.edu
sczhu@stat.ucla.edu
Abstract. This paper presents a method of learning reconfigurable hierarchical And-Or models to integrate context and occlusion for car detection. The And-Or model represents the regularities of car-to-car context and occlusion patterns at three levels: (i) layouts of spatially-coupled N cars, (ii) single cars with different viewpoint-occlusion configurations, and (iii) a small number of parts. The learning process consists of two stages. We first learn the structure of the And-Or model with three components: (a) mining N-car contextual patterns based on layouts of annotated single car bounding boxes, (b) mining the occlusion configurations based on the overlapping statistics between single cars, and (c) learning visible parts based on car 3D CAD simulation or heuristically mining latent car parts. The And-Or model is organized into a directed and acyclic graph which leads to the Dynamic Programming algorithm in inference. In the second stage, we jointly train the model parameters (for appearance, deformation and bias) using Weak-Label Structural SVM. In experiments, we test our model on four car datasets: the KITTI dataset [11], the street parking dataset [19], the PASCAL VOC2007 car dataset [7], and a self-collected parking lot dataset. We compare with state-of-the-art variants of deformable part-based models and other methods. Our model obtains significant improvement consistently on the four datasets.
Keywords: Car Detection, Context, Occlusion, And-Or Graph
LNCS 8694, p. 652 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014