A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Multi-Feature Hierarchical Template Matching Using Distance Transforms
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pedestrian Detection in Crowded Scenes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Aspect Detection of Articulated Objects
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Sharing Visual Features for Multiclass and Multiview Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo- and neural network-based pedestrian detection
IEEE Transactions on Intelligent Transportation Systems
Seeing the Objects Behind the Dots: Recognition in Videos from a Moving Camera
International Journal of Computer Vision
Boosting chamfer matching by learning chamfer distance normalization
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Accurate Object Recognition with Shape Masks
International Journal of Computer Vision
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Recognizing categories of articulated objects in real-world scenarios is a challenging problem for today's vision algorithms. Due to the large appearance changes and intra-class variability of these objects, it is hard to define a model, which is both general and discriminative enough to capture the properties of the category. In this work, we propose an approach, which aims for a suitable trade-off for this problem. On the one hand, the approach is made more discriminant by explicitly distinguishing typical object shapes. On the other hand, the method generalizes well and requires relatively few training samples by cross-articulation learning. The effectiveness of the approach is shown and compared to previous approaches on two datasets containing pedestrians with different articulations.