Counting people using video cameras
International Journal of Parallel, Emergent and Distributed Systems
Machine Vision and Applications
Real-Time Detection of Passing Objects Using Virtual Gate and Motion Vector Analysis
UIC '08 Proceedings of the 5th international conference on Ubiquitous Intelligence and Computing
Covariance Matrices for Crowd Behaviour Monitoring on the Escalator Exits
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
A Shape and Energy Based Approach to Vertical People Separation in Video Surveillance
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
PedVed: Pseudo Euclidian Distances for Video Events Detection
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
People counting in low density video sequences
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
Human detection in a challenging situation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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IEEE Transactions on Intelligent Transportation Systems
Simple camera calibration from a single image using five points on two orthogonal 1-D objects
IEEE Transactions on Image Processing
Expert Systems with Applications: An International Journal
Real-Time crowd density estimation using images
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Expert Systems with Applications: An International Journal
Visual knowledge transfer among multiple cameras for people counting with occlusion handling
Proceedings of the 20th ACM international conference on Multimedia
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In the past, the estimation of crowd density has become an important topic in the field of automatic surveillance systems. In this paper, the developed system goes one step further to estimate the number of people in crowded scenes in a complex background by using a single image. Therefore, more valuable information than crowd density can be obtained. There are two major steps in this system: recognition of the head-like contour and estimation of crowd size. First, the Haar wavelet transform is used to extract the featured area of the head-like contour, and then the support vector machine is used to classify these featured area as the contour of a head or not. Next, the perspective transforming technique of computer vision is used to estimate crowd size more accurately. Finally, a model world is constructed to test this proposed system and the system is also applied for real-world images