There is growing interest in video-based solutions for people monitoring and counting in business and security
applications. Compared to classic sensor-based solutions the
video-based ones allow for more versatile functionalities,
improved performance with lower costs. In this paper, we propose a real-time system for people counting
based on single low-end non-calibrated video camera.
The two main challenges addressed in this paper are: robust estimation of the scene background and the number
of real persons in merge-split scenarios. The latter is likely to occur whenever multiple persons move closely,
e.g. in shopping centers. Several persons may be considered to be a single person by automatic segmentation
algorithms, due to occlusions or shadows, leading to under-counting. Therefore, to account for noises, illumination
and static objects changes, a background substraction is performed using an adaptive background model
(updated over time based on motion information) and automatic thresholding. Furthermore, post-processing
of the segmentation results is performed, in the HSV color space, to remove shadows. Moving objects are
tracked using an adaptive Kalman filter, allowing a robust estimation of the objects future positions even under
heavy occlusion. The system is implemented in Matlab, and gives encouraging results even at high frame rates.
Experimental results obtained based on the PETS2006 datasets are presented at the end of the paper.