Authors:
Pieterjan De Potter
1
;
Philippe Belet
1
;
Chris Poppe
1
;
Steven Verstockt
2
;
Peter Lambert
1
and
Rik Van de Walle
1
Affiliations:
1
Ghent University - IBBT, Belgium
;
2
Ghent University - IBBT, University College West Flanders and Ghent University Association, Belgium
Keyword(s):
Video Analytics, Public Transport, People Counting, Histogram of Oriented Gradients, Kalman Filter.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Motion, Tracking and Stereo Vision
;
Video Surveillance and Event Detection
Abstract:
Automated people counting has multiple applications: referring passengers to vehicles with empty seats, gathering statistical information for railway companies to improve their distribution of vehicles, etc. In this paper, a people counting algorithm for public transport vehicles is presented. First, head-shoulder contours are detected by adaboost classification of a combination of a histogram of oriented gradients features and a color histogram. An integral histogram and integral image are used to speed up the extraction of these features. The results of the classification process are clustered and these clusters are tracked by a Kalman filter using a custom error covariance matrix. Finally, the path followed by an observed person is evaluated in order to count passengers entering and exiting the vehicle. Evaluation shows that this approach performs better than previous approaches, especially in scenarios with occlusions.