Depth Information Guided Crowd Counting for Complex Crowd Scenes

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

arXiv comment9 pages, 8 figures. The paper is under consideration at Pattern Recognition Letters

References in corpus (6)

• Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
• Semi-supervised Feature Analysis by Mining Correlations among Multiple Tasks
• Compound Rank-k Projections for Bilinear Analysis
• Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture
• CrowdNet: A Deep Convolutional Network for Dense Crowd Counting
• Towards Scene Understanding with Detailed 3D Object Representations