Application of improved transformer based on weakly supervised in crowd localization and crowd counting [PDF]
Scientific Reports, 2023 To the problem of the complex pre-processing and post-processing to obtain head-position existing in the current crowd localization method using pseudo boundary box and pre-designed positioning map, this work proposes an end-to-end crowd localization ...
Hui Gao +3 more
doaj +4 more sources
Cross-scale Vision Transformer for crowd localization
Journal of King Saud University: Computer and Information SciencesCrowd localization can provide the positions of individuals and the total number of people, which has great application value for security monitoring and public management, meanwhile it meets the challenges of lighting, occlusion and perspective effect ...
Shuang Liu +4 more
doaj +5 more sources
NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 In the last decade, crowd counting and localization attract much attention of researchers due to its wide-spread applications, including crowd monitoring, public safety, space design, etc. Many Convolutional Neural Networks (CNN) are designed for tackling this task.
Junyu Gao, Xuelong Li, Qi Wang
exaly +4 more sources
Wi-CaL: WiFi Sensing and Machine Learning Based Device-Free Crowd Counting and Localization
IEEE Access, 2022 Wireless sensing represented by WiFi channel state information (CSI) is now enabling various fields of applications such as person identification, human activity recognition, occupancy detection, localization, and crowd estimation these days.
Hyuckjin Choi +4 more
doaj +3 more sources
Crowd Counting and Localization Beyond Density Map
IEEE Access, 2022 Crowd analysis in general and counting in congested scenes, in particular, is an effective and vibrant research domain in computer vision due to its numerous applications.
Akbar Khan +6 more
doaj +2 more sources
Congested crowd instance localization with dilated convolutional swin transformer
Neurocomputing, 2022 10 pages, 4 ...
Maoguo Gong
exaly +3 more sources
Scale-aware Gaussian mixture loss for crowd localization transformers
High-Confidence ComputingA fundamental problem in crowd localization using computer vision techniques stems from intrinsic scale shifts. Scale shifts occur when the crowd density within an image is uneven and chaotic, a feature common in dense crowds.
Alabi Mehzabin Anisha, Sriram Chellappan
doaj +3 more sources
Enhancing medical response efficiency in real-time large crowd environments via smart coverage and deep learning for stable ecological health monitoring [PDF]
Scientific ReportsFestivals and city-wide mass events are prevalent in human societies worldwide, drawing large crowds. Such events range from concerts with a dozen attendees to large-scale actions with thousands of viewers.
Asma A. Alhashmi +7 more
doaj +2 more sources
An End-to-End Transformer Model for Crowd Localization
Lecture Notes in Computer Science, 2022 Crowd localization, predicting head positions, is a more practical and high-level task than simply counting. Existing methods employ pseudo-bounding boxes or pre-designed localization maps, relying on complex post-processing to obtain the head positions.
Xiang Bai
exaly +3 more sources
SRNet: Scale-Aware Representation Learning Network for Dense Crowd Counting
IEEE Access, 2021 Huge variations in the scales of people in images create an extremely challenging problem in the task of crowd counting. Currently, many researchers apply multi-column structures to solve the scale variation problem.
Liangjun Huang +4 more
doaj +1 more source