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Depth Information Guided Crowd Counting for Complex Crowd Scenes
- Publication Year :
- 2018
-
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<br />9 pages, 8 figures. The paper is under consideration at Pattern Recognition Letters
- 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
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....d3ccd66be2ca4adca73fd6f890cd30d6