A stochastic model of retinotopy: A self organizing process
Biological Cybernetics
Pfinder: real-time tracking of the human body
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
HUMO '00 Proceedings of the Workshop on Human Motion (HUMO'00)
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Counting Crowded Moving Objects
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A Viewpoint Invariant Approach for Crowd Counting
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Multicamera People Tracking with a Probabilistic Occupancy Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating pedestrian counts in groups
Computer Vision and Image Understanding
Crowd detection with a multiview sampler
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Counting People in Crowded Environments by Fusion of Shape and Motion Information
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
PETS2010 and PETS2009 Evaluation of Results Using Individual Ground Truthed Single Views
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
Robust Head-Shoulder Detection by PCA-Based Multilevel HOG-LBP Detector for People Counting
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
A method for counting moving people in video surveillance videos
EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
A Reliable People Counting System via Multiple Cameras
ACM Transactions on Intelligent Systems and Technology (TIST)
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
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Real-time high density people counter using morphological tools
IEEE Transactions on Intelligent Transportation Systems
A neural-based crowd estimation by hybrid global learning algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
People Counting and Human Detection in a Challenging Situation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Counting People in the Crowd Using a Generic Head Detector
AVSS '12 Proceedings of the 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance
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We present a people counting system that, based on the information gathered by multiple cameras, is able to tackle occlusions and lack of visibility that are typical in crowded and cluttered scenes. In our method, evidence of the foreground likelihood in each available view is obtained through a bio-inspired mechanism of self-organizing background subtraction, that is robust against well known foreground detection challenges and is able to detect both moving and stationary foreground objects. This information is gathered into a synergistic framework, that exploits the homography associated to each scene view and the scene ground plane, thus allowing to reconstruct people feet positions in a single ''feet map'' image. Finally, people counting is obtained by a k-NN classification, based on learning the count estimates from the feet maps, supported by a tracking mechanism that keeps track of people movements and of their identities along time, also enabling tolerance to occasional misdetections. Experimental results with detailed qualitative and quantitative analysis and comparisons with state-of-the-art methods are provided on publicly available benchmark datasets with different crowd densities and environmental conditions.