Fibonacci heaps and their uses in improved network optimization algorithms
Journal of the ACM (JACM)
PittPatt face detection and tracking for the CLEAR 2006 evaluation
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
Feature-centric evaluation for efficient cascaded object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Learning a restricted Bayesian network for object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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This paper describes Pittsburgh Pattern Recognition's participation in the face detection and tracking tasks for the CLEAR 2007 evaluation. Since CLEAR 2006, we have made substantial progress in optimizing our algorithms for speed, achieving better than real-time processing performance for a speed-up of more than 500× over the past two years. At the same time, we have maintained the high level of accuracy of our algorithm. In this paper, we first give a system overview, briefly explaining the three main stages of processing: (1) frame-based face detection; (2) motion-based tracking; and (3) track filtering. Second, we report our results, both in terms of accuracy and speed, over the CHIL and VACE test data sets. Finally, we offer some analysis on both speed and accuracy performance