Multi‐level feature fusion network for crowd counting
نویسندگان
چکیده
منابع مشابه
Feature Mining for Localised Crowd Counting
Crowd counting in public places has a wide spectrum of applications especially in crowd control, public space design, and pedestrian behaviour profiling. Existing counting by regression methods, which aim to learn a direct mapping between low-level features and people count without segregation or tracking of individuals, can be categorised into either global approaches or local approaches. Glob...
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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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Crowd counting is an important task in computer vision, which has many applications in video surveillance. Although the regression-based framework has achieved great improvements for crowd counting, how to improve the discriminative power of image representation is still an open problem. Conventional holistic features used in crowd counting often fail to capture semantic attributes and spatial ...
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This paper proposes an image textural analytical method for estimating the crowd density and counting. At first, the target detection is conducted to obtain the foreground image. This crowd image is used to calculate the gray level co-occurrence matrix (GLCM). Then, according to the characteristic values of the gray level co-occurrence matrix, i.e., energy, entropy, contrast, homogeneity, we us...
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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...
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ژورنال
عنوان ژورنال: IET Computer Vision
سال: 2021
ISSN: 1751-9632,1751-9640
DOI: 10.1049/cvi2.12012