AECNet: Attentive EfficientNet For Crowd Counting
2021 16TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC FACE AND GESTURE RECOGNITION (FG 2021)(2021)
Abstract
In the COVID pandemic situation, crowd counting became one of the tools to monitor if the social-distancing norms are being followed or not. However, in designing crowd counting algorithm, there are several challenges such as background noise, camera-to-objects distance, occlusion, and variations due to illumination, scale, and viewpoint. In this research, we propose a novel pipeline for density estimation in crowd counting. The proposed pipeline makes use of an encoder-decoder-based architecture in which we explore the family of EfficientNets for the encoder architecture. For the decoder, we propose a deeper attention network to assist the model in a better distinction between foreground and background pixels. We empirically show that for a crowd counting dataset, the use of average pooling operation for any backbone architecture of encoder gives a significant improvement in performance. In terms of Mean Absolute Error, the proposed pipeline outperforms existing state-of-the-art techniques by a large margin on large-scale and small-scale counting datasets, UCF-QNRF and UCF_CC_50 dataset. We also achieve state-of-the-art results on the ShanghaiTech and Mall datasets. We additionally propose a crowd counting dataset captured using drones. We perform benchmark experiments on this dataset with existing and the proposed methods.The proposed dataset can be found at http://www.iab-rubric.org/resources/CrowdUAV.html.
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Key words
crowd counting dataset,social-distancing norms,pipeline,encoder-decoder-based architecture,small-scale counting datasets,AECNet,EfficientNet,COVID pandemic,background pixels,attention network,mean absolute error
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