Scale-Adaptive Real-Time Crowd Detection and Counting for Drone Images

We propose a scale-adaptive crowd detection and counting approach for drone images. Based on local feature points and density estimation considering the image scale, we detect dense crowds over multiple distances and introduce an extremely fast counting strategy with high accuracy for our detected crowd regions. We compare our results with a recent CNN-based state-of-the-art approach and validate both methods for different scaling factors on a novel crowd dataset. The results show that our proposed method outperforms the pre-trained CNN-based approach and receives very precise counting results for different zoom factors, resolutions and crowd sizes. Its low computational complexity makes it highly suitable for real-time analysis or embedded systems.

[1]  Alberto Del Bimbo,et al.  Real-time people counting from depth imagery of crowded environments , 2014, 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[2]  Robert T. Collins,et al.  Marked point processes for crowd counting , 2009, CVPR.

[3]  Shenghua Gao,et al.  Single-Image Crowd Counting via Multi-Column Convolutional Neural Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Tobias Senst,et al.  Crowd analysis in non-static cameras using feature tracking and multi-person density , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[5]  Peter Reinartz,et al.  Automatic crowd analysis from airborne images , 2011, Proceedings of 5th International Conference on Recent Advances in Space Technologies - RAST2011.

[6]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[7]  Peter Reinartz,et al.  Automatic crowd density and motion analysis in airborne image sequences based on a probabilistic framework , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[8]  Peter Reinartz,et al.  KALMAN FILTER BASED FEATURE ANALYSIS FOR TRACKING PEOPLE FROM AIRBORNE IMAGES , 2012 .

[9]  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.

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

[11]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[12]  Shaogang Gong,et al.  From Semi-supervised to Transfer Counting of Crowds , 2013, 2013 IEEE International Conference on Computer Vision.

[13]  Vishal M. Patel,et al.  CNN-Based cascaded multi-task learning of high-level prior and density estimation for crowd counting , 2017, 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).