Crowd Counting Network Based on Feature Enhancement Loss and Foreground Attention [PDF]
Jisuanji kexue, 2023 Crowd counting aims to estimate the total number of people in an image and present its distribution accurately.The images in the relevant datasets usually involve a variety of scenes and include multiple people.To save labor,most datasets usually ...
ZHANG Yi, WU Qin
doaj +1 more source
Texture-based Homogeneity Analysis for Crowd Scene Modelling and Abnormality Detection [PDF]
Video-based crowd behaviour analysis techniques aim at tackling challenging problems such as detecting abnormal crowd behaviours and tracking specific individuals from complex real life scenes.
Xu, Zhijie, Wang, Jing
core +3 more sources
Evaluating Crowd Density Estimators Via Their Uncertainty Bounds [PDF]
2019 IEEE International Conference on Image Processing (ICIP), 2019 In this work, we use the Belief Function Theory which extends the probabilistic framework in order to provide uncertainty bounds to different categories of crowd density estimators. Our method allows us to compare the multi-scale performance of the estimators, and also to characterize their reliability for crowd monitoring applications requiring ...
Vandoni, Jennifer +2 more
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Advanced Pedestrian State Sensing Method for Automated Patrol Vehicle Based on Multi-Sensor Fusion
Sensors, 2022 At present, the COVID-19 pandemic still presents with outbreaks occasionally, and pedestrians in public areas are at risk of being infected by the viruses.
Pangwei Wang +3 more
doaj +1 more source
DMPNet: densely connected multi-scale pyramid networks for crowd counting [PDF]
PeerJ Computer Science, 2022 Crowd counting has been widely studied by deep learning in recent years. However, due to scale variation caused by perspective distortion, crowd counting is still a challenging task.
Pengfei Li +3 more
doaj +2 more sources
Improved Crowd Counting Method Based on Scale-Adaptive Convolutional Neural Network
IEEE Access, 2019 Crowd counting is a challenging task due to the influence of various factors, such as scene transformation, complex crowd distribution, uneven illumination, and occlusion.
Jun Sang +6 more
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Context-Aware Multi-Scale Aggregation Network for Congested Crowd Counting
Sensors, 2022 In this paper, we propose a context-aware multi-scale aggregation network named CMSNet for dense crowd counting, which effectively uses contextual information and multi-scale information to conduct crowd density estimation.
Liangjun Huang +4 more
doaj +1 more source
Scientific Exploration for Density Estimation and Crowd Counting of Crowded Scene
Journal of Physics: Conference Series, 2021 Abstract Crowd density estimation model is a typical concept which compute the counting of the people in the crowded image. There are many of research papers are written in this area to solve the different kind of real world problems. This paper shows the state of art and different types of framework proposed on density estimation and ...
Juginder Pal Singh +4 more
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Cascaded Multi-Task Learning of Head Segmentation and Density Regression for RGBD Crowd Counting
IEEE Access, 2020 In this paper we propose a novel regression based RGBD crowd counting method. Compared with previous RGBD crowd counting methods which mainly exploit depth cue to facilitate person/head detection, our approach adopts density map regression and is more ...
Desen Zhou, Qian He
doaj +1 more source
Density Estimation and Crowd Counting
CoRRThis study enhances a crowd density estimation algorithm originally designed for image-based analysis by adapting it for video-based scenarios. The proposed method integrates a denoising probabilistic model that utilizes diffusion processes to generate high-quality crowd density maps.
Balachandra Devarangadi Sunil +2 more
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