Active shape models—their training and application
Computer Vision and Image Understanding
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Real Time Robust Human Detection and Tracking System
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Real-Time Human Detection, Tracking, and Verification in Uncontrolled Camera Motion Environments
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
People detection and tracking through stereo vision for human-robot interaction
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Robust real-time face detection using face certainty map
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Real time multiple people tracking and pose estimation
Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis
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This paper proposes a pose robust human detection method from a sequence of stereo images using the multiple oriented 2D elliptical filters (MO2DEFs), which can detect the humans regardless of the their scales and poses. Existing object oriented scale adaptive filter (OOSAF) has some disadvantages since they cannot detect the human with an arbitrary pose. To overcome this limitation, we introduce the pose robust MO2DEFs whose shapes are the oriented ellipses. We perform human detection by applying four 2D elliptical filters with specific orientations to the 2D spatial-depth histogram and by taking the thresholds over the filtered histograms. In addition, we determine the human pose by taking the orientation of the 2D elliptical filter whose convolution result is maximal among the MO2DEFs. We verify the human candidates by either detecting the face or matching head-shoulder shapes over the segmented human candidates of the selected rotation. The experimental results show that (1) the accuracy of pose angle estimation is about 88%, (2) the human detection using the proposed MO2DEFs outperforms that of using the existing OOSAF by 15~20%, especially in case of the posed human.