Counting people by clustering person detector outputs
We present a people counting system that estimates the number of people in a scene by employing a clustering scheme based on Dirichlet Process Mixture Models (DP-MMs) which takes outputs of a person detector system as input. For each frame, we run a person detector on the frame, take its output as a set of detection areas and define a set of features based on spatial, color and temporal information for each detection. Then using these features, we cluster the detections using DPMMs and Gibbs...[Show more]
|Collections||ANU Research Publications|
|Source:||11th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2014|
|01_Topkaya_Counting_people_by_clustering_2014.pdf||1.91 MB||Adobe PDF||Request a copy|
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