Abstract
Crowd counting is an active research area within scene analysis. Over the last 20 years, researchers proposed various algorithms for crowd counting in real-time scenarios due to many applications in disaster management systems, public events, safety monitoring, and so on. In our paper, we proposed an end-to-end semantic segmentation framework for crowd counting in a dense crowded image. Our proposed framework was based on semantic scene segmentation using an optimized convolutional neural network. The framework successfully highlighted the foreground and suppressed the background part. The framework encoded the high-density maps through a guided attention mechanism system. We obtained crowd counting through integrating the density maps. Our proposed algorithm classified the crowd counting in each image into groups to adapt the variations occurring in crowd counting. Our algorithm overcame the scale variations of a crowded image through multi-scale features extracted from the images. We conducted experiments with four standard crowd-counting datasets, reporting better results as compared to previous results.
Highlights
The challenging and meaningful task of precisely estimating the number of objects and persons in an image has several applications in the Computer Vision (CV) domain
Crowd counting is an essential task in crowd image analysis due to the very diverse applications
Crowd counting is challenging when the proposed algorithm is exposed to data collected in diverse conditions and in-the-wild
Summary
The challenging and meaningful task of precisely estimating the number of objects and persons in an image has several applications in the Computer Vision (CV) domain. An accurate crowd count helps in emergency situations such as stampedes and fire events Considering these factors, many researchers are inclined to explore image-based object counting and its applications in various fields. The structural and distribution patterns of all such applications are in some ways similar to each other, the improvement in one application implies the improvement in other related applications This implies that crowd counting methods can be extended to crowd analysis applications including flow analysis, density estimation, crowd monitoring, and son on. Our proposed method highlighted the head region by suppressing the non-head part through a novel optimized loss function This guided sort of mechanism pays comparatively more attention to the head part and encodes the specific refined density map. We performed extensive experiments on four standard datasets, reporting better results as compared to previous results
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