Robust Real-Time Face Detection
International Journal of Computer Vision
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Counting Crowded Moving Objects
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A Viewpoint Invariant Approach for Crowd Counting
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
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
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
A Method for Counting People in Crowded Scenes
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
Counting Moving People in Videos by Salient Points Detection
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
CRV '12 Proceedings of the 2012 Ninth Conference on Computer and Robot Vision
Predicting occupation via human clothing and contexts
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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A counting approach for crowd flow based on feature points is proposed. The objective is to obtain the characteristics of the crowd flow in a scene, including the crowd orientation and numeric count. For the feature point detection, a three-frame difference algorithm is used to obtain a foreground containing only the moving objects. Therefore, after the SURF feature point detection, only the feature points of the foreground are retained for further processing. This greatly reduces the time complexity of the SURF algorithm. For feature point clustering, we present an improved DBSCAN clustering algorithm in which the non-motion feature points are further eliminated and only the remaining feature points are clustered. For the calculation of the crowd flow orientation, the feature points are tracked based on a local Lucas-Kanade optical flow with Hessian matrix algorithm. In the crowd flow number counting, the crowd eigenvectors are constructed based on the SURF feature points and are trained using a support vector regression machine. The experimental results show that the proposed crowd orientation and counting method are more robust and provide crowd flow statistics with higher accuracy than previous approaches.