Feature Mining for Localised Crowd Counting

This paper presents a multi-output regression model for crowd counting in public scenes. Existing counting by regression methods either learn a single model for global counting, or train a large number of separate regressors for localised density estimation. In contrast, our single regression model based approach is able to estimate people count in spatially localised regions and is more scalable without the need for training a large number of regressors proportional to the number of local regions. In particular, the proposed model automatically learns the functional mapping between interdependent low-level features and multi-dimensional structured outputs. The model is able to discover the inherent importance of different features for people counting at different spatial locations. Extensive evaluations on an existing crowd analysis benchmark dataset and a new more challenging dataset demonstrate the effectiveness of our approach.

[1]  Roberto Cipolla,et al.  Unsupervised Bayesian Detection of Independent Motion in Crowds , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[2]  Alexander Gammerman,et al.  Ridge Regression Learning Algorithm in Dual Variables , 1998, ICML.

[3]  Lei Huang,et al.  Crowd density analysis using co-occurrence texture features , 2010, 5th International Conference on Computer Sciences and Convergence Information Technology.

[4]  Tommy W. S. Chow,et al.  A neural-based crowd estimation by hybrid global learning algorithm , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[5]  Robert T. Collins,et al.  Marked point processes for crowd counting , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Hai Tao,et al.  Counting Pedestrians in Crowds Using Viewpoint Invariant Training , 2005, BMVC.

[7]  Cristian Sminchisescu,et al.  Structural SVM for visual localization and continuous state estimation , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[8]  Sridha Sridharan,et al.  Crowd Counting Using Multiple Local Features , 2009, 2009 Digital Image Computing: Techniques and Applications.

[9]  Tieniu Tan,et al.  Estimating the number of people in crowded scenes by MID based foreground segmentation and head-shoulder detection , 2008, 2008 19th International Conference on Pattern Recognition.

[10]  A. Marana,et al.  Estimation of crowd density using image processing , 1997 .

[11]  Y. Haitovsky.  On multivariate ridge regression , 1987 .

[12]  Ramakant Nevatia,et al.  Segmentation and Tracking of Multiple Humans in Crowded Environments , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Serge J. Belongie,et al.  Counting Crowded Moving Objects , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[14]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[15]  Nuno Vasconcelos,et al.  Privacy preserving crowd monitoring: Counting people without people models or tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Christoph H. Lampert,et al.  Learning to Localize Objects with Structured Output Regression , 2008, ECCV.

[17]  Andrew Zisserman,et al.  Learning To Count Objects in Images , 2010, NIPS.

[18]  Dirk Helbing,et al.  Pedestrian, Crowd and Evacuation Dynamics , 2013, Encyclopedia of Complexity and Systems Science.

[19]  Yangsheng Xu,et al.  Crowd Density Estimation Using Texture Analysis and Learning , 2006, 2006 IEEE International Conference on Robotics and Biomimetics.

[20]  Luciano da Fontoura Costa,et al.  Estimating crowd density with Minkowski fractal dimension , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[21]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[22]  Thorsten Joachims,et al.  Cutting-plane training of structural SVMs , 2009, Machine Learning.

[23]  Nuno Vasconcelos,et al.  Counting People With Low-Level Features and Bayesian Regression , 2012, IEEE Transactions on Image Processing.

[24]  Svetha Venkatesh,et al.  Face Recognition Using Kernel Ridge Regression , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.