WebMar 28, 2024 · CNN-based Density Estimation and Crowd Counting: A Survey. Guangshuai Gao, Junyu Gao, Qingjie Liu, Qi Wang, Yunhong Wang. Accurately estimating the number of objects in a single image is a challenging yet meaningful task and has been applied in many applications such as urban planning and public safety. In the various … WebMay 29, 2024 · Crowd counting is useful for crowd management and control to avoid massive stampedes caused by overcrowding in restricted public places. As crowd gathering has become increasingly more common in recent years, the counting task has received considerable attention in a variety of applications. For instance, intelligent surveillance …

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WebMar 2, 2024 · Abstract: We propose a multitask approach for crowd counting and person localization in a unified framework. As the detection and localization tasks are well-correlated and can be jointly tackled, our … WebApr 9, 2024 · Supervised crowd counting relies heavily on costly manual labeling, which is difficult and expensive, especially in dense scenes. To alleviate the problem, we propose a novel unsupervised framework for crowd counting, named CrowdCLIP. The core idea is built on two observations: 1) the recent contrastive pre-trained vision-language model … grocery store honey ham price

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Web1 day ago · Crowd Counting with Sparse Annotation. Shiwei Zhang, Zhengzheng Wang, Qing Liu, Fei Wang, Wei Ke, Tong Zhang. This paper presents a new annotation method called Sparse Annotation (SA) for crowd counting, which reduces human labeling efforts by sparsely labeling individuals in an image. We argue that sparse labeling can reduce the … WebAug 6, 2024 · Crowd counting is a challenging problem due to the diverse crowd distribution and background interference. In this paper, we propose a new approach for head size estimation to reduce the impact of different crowd scale and background noise. Different from just using local information of distance between human heads, the global … WebTo alleviate the problem, we propose a novel unsupervised framework for crowd counting, named CrowdCLIP. The core idea is built on two observations: 1) the recent contrastive pre-trained vision-language model (CLIP) has presented impressive performance on various downstream tasks; 2) there is a natural mapping between crowd patches and count text. grocery store holden beach nc