A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Haar Features for FACS AU Recognition
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pedestrian Detection and Tracking for Counting Applications in Crowded Situations
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
Analysis of Time Series via their Linguistic Summarization: the Use of the Sugeno Integral
ISDA '07 Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications
Dynamic image sequence analysis using fuzzy measures
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper proposes a visitor counter system which is capable of counting single & multiple object visitors using fuzzy measure theory for tracking and Boosting method for classification. Besides fuzzy measure theory, the paper also uses Euclidean distance to track a visitor based on his movement between each frame, while fuzzy measure theory tracks a visitor based on trust degree. Both system performances are compared for their visitor tracking accuracy and their computational speed. Experimental results show that Euclidean distance and Fuzzy measure have similar accuracy for tracking visitor. However, Euclidean distance is faster than those of fuzzy measure theory in the computational speed. The proposed visitor counter system can be further developed for real-time visitor counting in shopping mall, station, and other places.