Abstract
Video-based people detection and tracking is an important task for a wide variety of applications concerning computer vision systems. In this work, we propose a pedestrian tracking-by-detection system focused on the role of computational performance. To this aim, we have developed a computationally efficient method for people detection, based on background subtraction and image density projections. Tracking is performed by a set of trackers based on particle filters that are properly associated with detections. We test our system on different well-known benchmark datasets. Experimental results reveal that the proposed method is efficient and effective. Specifically, it obtains a processing rate of 22 frames per second on average when tracking a maximum number of 9 people.
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Lacabex, B., Cuesta-Infante, A., Montemayor, A.S., Pantrigo, J.J. (2015). Pedestrian Tracking-by-Detection Using Image Density Projections and Particle Filters. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Bioinspired Computation in Artificial Systems. IWINAC 2015. Lecture Notes in Computer Science(), vol 9108. Springer, Cham. https://doi.org/10.1007/978-3-319-18833-1_18
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DOI: https://doi.org/10.1007/978-3-319-18833-1_18
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18832-4
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