  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
