1. Depth Information Guided Crowd Counting for Complex Crowd Scenes
- Author
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Pei Lv, Bing Zhou, Gaoge Cui, Changsheng Xu, Mingliang Xu, Xiaoheng Jiang, and Zhaoyang Ge
- Subjects
FOS: Computer and information sciences ,business.industry ,Computer science ,Computer Vision and Pattern Recognition (cs.CV) ,Pedestrian detection ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Computer Science - Computer Vision and Pattern Recognition ,02 engineering and technology ,Density estimation ,01 natural sciences ,Image (mathematics) ,Artificial Intelligence ,0103 physical sciences ,Signal Processing ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Computer vision ,Computer Vision and Pattern Recognition ,Artificial intelligence ,Crowd density ,010306 general physics ,business ,Software ,Crowd counting - Abstract
It is important to monitor and analyze crowd events for the sake of city safety. In an EDOF (extended depth of field) image with a crowded scene, the distribution of people is highly imbalanced. People far away from the camera look much smaller and often occlude each other heavily, while people close to the camera look larger. In such a case, it is difficult to accurately estimate the number of people by using one technique. In this paper, we propose a Depth Information Guided Crowd Counting (DigCrowd) method to deal with crowded EDOF scenes. DigCrowd first uses the depth information of an image to segment the scene into a far-view region and a near-view region. Then Digcrowd maps the far-view region to its crowd density map and uses a detection method to count the people in the near-view region. In addition, we introduce a new crowd dataset that contains 1000 images. Experimental results demonstrate the effectiveness of our DigCrowd method, 9 pages, 8 figures. The paper is under consideration at Pattern Recognition Letters
- Published
- 2018