Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Who? When? Where? What? A Real Time System for Detecting and Tracking People
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Segmentation and Tracking of Interacting Human Body Parts under Occlusion and Shadowing
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Detecting Moving Shadows: Algorithms and Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Adaptive Gaussian Mixture Model for Background Subtraction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
ACM Computing Surveys (CSUR)
A Fast Algorithm for Real-time Video Tracking
IITA '07 Proceedings of the Workshop on Intelligent Information Technology Application
Tracking People in Crowds by a Part Matching Approach
AVSS '08 Proceedings of the 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance
Motion-based background subtraction using adaptive kernel density estimation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
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This paper presents a robust adaptive moving human detection and recognition method in videos. The human detection method consists of modified moving average background model with supportive secondary model and an adaptive threshold selection model based on Gaussian distribution. The moving average background model is used for background modeling and the background subtraction system is used to provide foreground image through difference image between current image and background model. The adaptive threshold method is used to simultaneously update the system to environment changes. The modified human model consists of five parts with robust features to facilitate human recognition process. For recognition purpose Support Vector Machine has been used as classifier. Experimental results show the effectiveness of proposed system.