An Enhanced Scale Robust Network for Crowd Counting
نویسندگان
چکیده
منابع مشابه
Crowd counting via scale-adaptive convolutional neural network
The task of crowd counting is to automatically estimate the pedestrian number in crowd images. To cope with the scale and perspective changes that commonly exist in crowd images, state-of-the-art approaches employ multi-column CNN architectures to regress density maps of crowd images. Multiple columns have different receptive fields corresponding to pedestrians (heads) of different scales. We i...
متن کاملSupplementary Material : Switching Convolutional Neural Network for Crowd Counting
Differential training on the CNN regressors R1 through R3 generates a multichotomy that minimizes the predicted count by choosing the best regressor for a given crowd scene patch. However, the trained switch is not ideal and the manifold separating the space of patches is complex to learn (see Section 5.2 of the main paper). To mitigate the effect of switch inaccuracy and inherent complexity of...
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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...
متن کاملUnsupervised Crowd Counting
Most crowd counting methods rely on training with labeled data to learn a mapping between image features and the number of people in the scene. However, the nature of this mapping may change as a function of the scene, camera parameters, illumination etc., limiting the ability of such supervised systems to generalize to novel conditions. Here we propose an alternative, unsupervised strategy. Th...
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A quadratic programming method with network flow constraints is proposed to improve crowd pedestrian counting in video surveillance. Most of the existing approaches estimate the number of pedestrians within one frame, which result in inconsistent predictions in temporal domain. In this paper, firstly, we segment the foreground of each frame into different groups, each of which contains several ...
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.2977380