Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/609297
Title: Learning And-Or Model to Represent Context and Occlusion for Car Detection and Viewpoint Estimation
Authors: Tianfu Wu;Bo Li;Song-Chun Zhu
subject: context|car viewpoint estimation|and-or graph|occlusion modeling|hierarchical model|Car detection
Year: 2016
Publisher: IEEE
Abstract: This paper presents a method for learning an And-Or model to represent context and occlusion for car detection and viewpoint estimation. The learned And-Or model represents car-to-car context and occlusion configurations at three levels: (i) spatially-aligned cars, (ii) single car under different occlusion configurations, and (iii) a small number of parts. The And-Or model embeds a grammar for representing large structural and appearance variations in a reconfigurable hierarchy. The learning process consists of two stages in a weakly supervised way (i.e., only bounding boxes of single cars are annotated). First, the structure of the And-Or model is learned with three components: (a) mining multi-car contextual patterns based on layouts of annotated single car bounding boxes, (b) mining occlusion configurations between single cars, and (c) learning different combinations of part visibility based on CAD simulations. The And-Or model is organized in a directed and acyclic graph which can be inferred by Dynamic Programming. Second, the model parameters (for appearance, deformation and bias) are jointly trained using Weak-Label Structural SVM. In experiments, we test our model on four car detection datasets-the KITTI dataset [1] , the PASCAL VOC2007 car dataset [2] , and two self-collected car datasets, namely the Street-Parking car dataset and the Parking-Lot car dataset, and three datasets for car viewpoint estimation-the PASCAL VOC2006 car dataset [2] , the 3D car dataset [3] , and the PASCAL3D+ car dataset [4] . Compared with state-of-the-art variants of deformable part-based models and other methods, our model achieves significant improvement consistently on the four detection datasets, and comparable performance on car viewpoint estimation.
Description: 
URI: http://localhost/handle/Hannan/148081
http://localhost/handle/Hannan/609297
ISSN: 0162-8828
volume: 38
issue: 9
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7317819.pdf1.85 MBAdobe PDFThumbnail
Preview File
Title: Learning And-Or Model to Represent Context and Occlusion for Car Detection and Viewpoint Estimation
Authors: Tianfu Wu;Bo Li;Song-Chun Zhu
subject: context|car viewpoint estimation|and-or graph|occlusion modeling|hierarchical model|Car detection
Year: 2016
Publisher: IEEE
Abstract: This paper presents a method for learning an And-Or model to represent context and occlusion for car detection and viewpoint estimation. The learned And-Or model represents car-to-car context and occlusion configurations at three levels: (i) spatially-aligned cars, (ii) single car under different occlusion configurations, and (iii) a small number of parts. The And-Or model embeds a grammar for representing large structural and appearance variations in a reconfigurable hierarchy. The learning process consists of two stages in a weakly supervised way (i.e., only bounding boxes of single cars are annotated). First, the structure of the And-Or model is learned with three components: (a) mining multi-car contextual patterns based on layouts of annotated single car bounding boxes, (b) mining occlusion configurations between single cars, and (c) learning different combinations of part visibility based on CAD simulations. The And-Or model is organized in a directed and acyclic graph which can be inferred by Dynamic Programming. Second, the model parameters (for appearance, deformation and bias) are jointly trained using Weak-Label Structural SVM. In experiments, we test our model on four car detection datasets-the KITTI dataset [1] , the PASCAL VOC2007 car dataset [2] , and two self-collected car datasets, namely the Street-Parking car dataset and the Parking-Lot car dataset, and three datasets for car viewpoint estimation-the PASCAL VOC2006 car dataset [2] , the 3D car dataset [3] , and the PASCAL3D+ car dataset [4] . Compared with state-of-the-art variants of deformable part-based models and other methods, our model achieves significant improvement consistently on the four detection datasets, and comparable performance on car viewpoint estimation.
Description: 
URI: http://localhost/handle/Hannan/148081
http://localhost/handle/Hannan/609297
ISSN: 0162-8828
volume: 38
issue: 9
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7317819.pdf1.85 MBAdobe PDFThumbnail
Preview File
Title: Learning And-Or Model to Represent Context and Occlusion for Car Detection and Viewpoint Estimation
Authors: Tianfu Wu;Bo Li;Song-Chun Zhu
subject: context|car viewpoint estimation|and-or graph|occlusion modeling|hierarchical model|Car detection
Year: 2016
Publisher: IEEE
Abstract: This paper presents a method for learning an And-Or model to represent context and occlusion for car detection and viewpoint estimation. The learned And-Or model represents car-to-car context and occlusion configurations at three levels: (i) spatially-aligned cars, (ii) single car under different occlusion configurations, and (iii) a small number of parts. The And-Or model embeds a grammar for representing large structural and appearance variations in a reconfigurable hierarchy. The learning process consists of two stages in a weakly supervised way (i.e., only bounding boxes of single cars are annotated). First, the structure of the And-Or model is learned with three components: (a) mining multi-car contextual patterns based on layouts of annotated single car bounding boxes, (b) mining occlusion configurations between single cars, and (c) learning different combinations of part visibility based on CAD simulations. The And-Or model is organized in a directed and acyclic graph which can be inferred by Dynamic Programming. Second, the model parameters (for appearance, deformation and bias) are jointly trained using Weak-Label Structural SVM. In experiments, we test our model on four car detection datasets-the KITTI dataset [1] , the PASCAL VOC2007 car dataset [2] , and two self-collected car datasets, namely the Street-Parking car dataset and the Parking-Lot car dataset, and three datasets for car viewpoint estimation-the PASCAL VOC2006 car dataset [2] , the 3D car dataset [3] , and the PASCAL3D+ car dataset [4] . Compared with state-of-the-art variants of deformable part-based models and other methods, our model achieves significant improvement consistently on the four detection datasets, and comparable performance on car viewpoint estimation.
Description: 
URI: http://localhost/handle/Hannan/148081
http://localhost/handle/Hannan/609297
ISSN: 0162-8828
volume: 38
issue: 9
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7317819.pdf1.85 MBAdobe PDFThumbnail
Preview File