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Divide and Count: Generic Object Counting by Image Divisions
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DOI：10.1109/TIP.2018.2875353.png)
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摘要
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We propose a general object counting method that does not use any prior category information. We learn from local image divisions to predict global image-level counts without using any form of local annotations. Our method separates the input image into a set of image divisions-each fully covering the image. Each image division is composed of a set of region proposals or uniform grid cells. Our approach learns in an endto-end deep learning architecture to predict global image-level counts from local image divisions. The method incorporates a counting layer which predicts object counts in the complete image, by enforcing consistency in counts when dealing with overlapping image regions. Our counting layer is based on the inclusion-exclusion principle from set theory. We analyze the individual building blocks of our proposed approach on Pascal-VOC2007 and evaluate our method on the MS-COCO large scale generic object data set as well as on three class-specific counting data sets: UCSD pedestrian data set, and CARPK, and PUCPR+ car data sets.
Keyword:
Generic-class object counting
inclusion-exclusion principle
regression
fully convolutional networks
counting with region proposals
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期刊
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13.7
论文数:
1.0W
被引数:
8.4W
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