A Sparse Regression Mixture Model for Clustering Time-Series
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
Towards Generic Detection of Unusual Events in Video Surveillance
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Feature clustering for vehicle detection and tracking in road traffic surveillance
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Feature-based tracking approach for detection of moving vehicle in traffic videos
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Clustering of trajectories in video surveillance using growing neural gas
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
Annotated free-hand sketches for video retrieval using object semantics and motion
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Template matching and monte carlo markova chain for people counting under occlusions
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
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In this paper, we propose the use of lustering methods for automatic counting of pedestrians in video sequences. As input, we consider the output of those detection/tracking systems that overestimate the number of targets. Clustering techniques are applied to the resulting trajectories in order to reduce the bias between the number of tracks and the real number of targets. The main hypothesis is that those trajectories belonging to the same human body are more similar than trajectories belonging to different individuals. Several data representations and different distance/similarity measures are proposed and compared, under a common hierarchical clustering framework, and both quantitative and qualitative results are presented