Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating pedestrian counts in groups
Computer Vision and Image Understanding
Real-Time People Counting Using Multiple Lines
WIAMIS '08 Proceedings of the 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services
Robust pedestrian detection and tracking in crowded scenes
Image and Vision Computing
A People Counting System Based on Face Detection and Tracking in a Video
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
An area-based decision rule for people-counting systems
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Real-time high density people counter using morphological tools
IEEE Transactions on Intelligent Transportation Systems
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We extract local empirical templates and density ratios from a large collection of surveillance videos, and develop a fast and lowcost scheme for people counting. The local empirical templates are extracted by clustering the foregrounds induced by single pedestrians with similar features in silhouettes. The density ratio is obtained by comparing the size of the foreground induced by a group of pedestrians to that of the local empirical template considered the most appropriate for the region where the group foreground is captured. Because of the local scale normalization between sizes, the density ratio appears to have a bound closely related to the number of pedestrians that induce the group foreground. We estimate the bounds of density ratios for groups of different numbers of pedestrians in the learning phase, and use the estimated bounds to count the pedestrians in online settings. The results are promising.