Pedestrian Detection Using Wavelet Templates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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
Feature-Based Sequence-to-Sequence Matching
International Journal of Computer Vision
Covariance Tracking using Model Update Based on Lie Algebra
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Multi-texture modeling of 3D traffic scenes
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Multicamera People Tracking with a Probabilistic Occupancy Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multiview approach to tracking people in crowded scenes using a planar homography constraint
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Region covariance: a fast descriptor for detection and classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Cascade of descriptors to detect and track objects across any network of cameras
Computer Vision and Image Understanding
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This work tackles the challenge of detecting and matching objects in scenes observed simultaneously by fixed and mobile cameras. No calibration between the cameras is needed, and no training data is used. A fully automated system is presented to detect if an object, observed by a fixed camera, is seen by a mobile camera and where it is localized in its image plane. Only the observations from the fixed camera are used. An object descriptor based on grids of region descriptors is used in a cascade manner. Fixed and mobile cameras collaborate to confirm detection. Detected regions in the mobile camera are validated by analyzing the dual problem: analyzing their corresponding most similar regions in the fixed camera to check if they coincide with the object of interest. Experiments show that objects are successfully detected even if the cameras have significant change in image quality, illumination, and viewpoint. Qualitative and quantitative results are presented in indoor and outdoor urban scenes.