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.
CITATION STYLE
Lacabex, B., Cuesta-Infante, A., Montemayor, A. S., & Pantrigo, J. J. (2015). Pedestrian tracking-by-detection using image density projections and particle filters. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9108, pp. 166–174). Springer Verlag. https://doi.org/10.1007/978-3-319-18833-1_18
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