Kernel-based Density Map Generation for Dense Object Counting

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

64 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)1357-1370
Number of pages14
Journal / PublicationIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume44
Issue number3
Online published9 Sept 2020
Publication statusPublished - Mar 2022

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Abstract

Crowd counting is an essential topic in computer vision due to its practical usage in surveillance systems. The typical design of crowd counting algorithms is divided into two steps. First, the ground-truth density maps of crowd images are generated from the ground-truth dot maps (density map generation), e.g., by convolving with a Gaussian kernel. Second, deep learning models are designed to predict a density map from an input image (density map estimation). The density map based counting methods that incorporate density map as the intermediate representation have improved counting performance dramatically. However, in the sense of end-to-end training, the hand-crafted methods used for generating the density maps may not be optimal for the particular network or dataset used. To address this issue, we propose an adaptive density map generator, which takes the annotation dot map as input, and learns a density map representation for a counter. The counter and generator are trained jointly within an end-to-end framework. We also show that the proposed framework can be applied to general dense object counting tasks. Extensive experiments are conducted on 10 datasets for 3 applications: crowd counting, vehicle counting, and general object counting. The experiment results on these datasets confirm the effectiveness of the proposed learnable density map representations.

Research Area(s)

  • Crowd counting, vehicle counting, object counting, density map generation, density map estimation, deep learning

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