- Research Article
- Open Access
Detection and Tracking of Humans and Faces
EURASIP Journal on Image and Video Processing volume 2008, Article number: 526191 (2007)
We present a video analysis framework that integrates prior knowledge in object tracking to automatically detect humans and faces, and can be used to generate abstract representations of video (key-objects and object trajectories). The analysis framework is based on the fusion of external knowledge, incorporated in a person and in a face classifier, and low-level features, clustered using temporal and spatial segmentation. Low-level features, namely, color and motion, are used as a reliability measure for the classification. The results of the classification are then integrated into a multitarget tracker based on a particle filter that uses color histograms and a zero-order motion model. The tracker uses efficient initialization and termination rules and updates the object model over time. We evaluate the proposed framework on standard datasets in terms of precision and accuracy of the detection and tracking results, and demonstrate the benefits of the integration of prior knowledge in the tracking process.
To access the full article, please see PDF.
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
About this article
Cite this article
Karlsson, S., Taj, M. & Cavallaro, A. Detection and Tracking of Humans and Faces. J Image Video Proc 2008, 526191 (2007). https://doi.org/10.1155/2008/526191
- Prior Knowledge
- Analysis Framework
- Particle Filter
- Motion Model
- Object Tracking