W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Pedestrians Using Patterns of Motion and Appearance
International Journal of Computer Vision
Machine Vision and Applications
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Adaptive model for robust pedestrian counting
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Robust people counting in video surveillance: Dataset and system
AVSS '11 Proceedings of the 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance
Modeling of moving object trajectory by spatio-temporal learning for abnormal behavior detection
AVSS '11 Proceedings of the 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance
A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance
IEEE Transactions on Circuits and Systems for Video Technology
Predicting Pedestrian Counts in Crowded Scenes With Rich and High-Dimensional Features
IEEE Transactions on Intelligent Transportation Systems
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Reliable estimation of number of pedestrians has played an important role in the management of public places. However, how to accurately count pedestrians with abnormal behavior noises is one challenge in such surveillance systems. To deal with this problem, we propose a new and efficient framework for pedestrian analysis and counting, which consists of two main steps. Firstly, a rule induction classifier with optical-flow feature is designed to recognize the abnormal behaviors. Then, a linear regression model is used to learn the relationship between the number of pixels and the number of pedestrians. Consequently, our system can count pedestrians precisely in general scenes without the influence of abnormal behaviors. Experimental results on the videos of different scenes show that our system has achieved an accuracy of 98.59% and 96.04% for the abnormal behavior recognition and pedestrian counting respectively. Furthermore, it is robust against the variation of lighting and noise.