Anthropocentric video analysis for film and games postproduction
Proceedings of the 11th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing on International Conference on Computer Systems and Technologies
Fast and accurate pedestrian detection using a cascade of multiple features
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
A Reliable People Counting System via Multiple Cameras
ACM Transactions on Intelligent Systems and Technology (TIST)
Shape matching using a binary search tree structure of weak classifiers
Pattern Recognition
Shape space estimation by SOM2
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Pedestrian image segmentation via shape-prior constrained random walks
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II
Fast Human Pose Detection Using Randomized Hierarchical Cascades of Rejectors
International Journal of Computer Vision
Improving HOG with image segmentation: application to human detection
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
ROI-HOG and LBP based human detection via shape part-templates matching
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Object class detection: A survey
ACM Computing Surveys (CSUR)
Computer Vision and Image Understanding
A hierarchical algorithm for fuzzy template matching in emotional facial images
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Computational intelligence models for image processing and information reasoning
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We propose a shape-based, hierarchical part-template matching approach to simultaneous human detection and segmentation combining local part-based and global shape-template-based schemes. The approach relies on the key idea of matching a part-template tree to images hierarchically to detect humans and estimate their poses. For learning a generic human detector, a pose-adaptive feature computation scheme is developed based on a tree matching approach. Instead of traditional concatenation-style image location-based feature encoding, we extract features adaptively in the context of human poses and train a kernel-SVM classifier to separate human/nonhuman patterns. Specifically, the features are collected in the local context of poses by tracing around the estimated shape boundaries. We also introduce an approach to multiple occluded human detection and segmentation based on an iterative occlusion compensation scheme. The output of our learned generic human detector can be used as an initial set of human hypotheses for the iterative optimization. We evaluate our approaches on three public pedestrian data sets (INRIA, MIT-CBCL, and USC-B) and two crowded sequences from Caviar Benchmark and Munich Airport data sets.