Drone-captured crowd videos have become increasingly prevalent in various applications in recent years, including crowd density estimation via measuring crowd collectiveness. Traditional methods often measure local differences in motion directions am...
Automatically estimating the number of people in unconstrained scenes is a crucial yet challenging task in different real-world applications, including video surveillance, public safety, urban planning, and traffic monitoring. In addition, methods de...
The estimation of crowd density is crucial for applications such as autonomous driving, visual surveillance, crowd control, public space planning, and warning visually distracted drivers prior to an accident. Having strong translational, reflective,...
Crowd counting is a significant task in computer vision. By combining the rich texture information from RGB images with the insensitivity to illumination changes offered by thermal imaging, the applicability of models in real-world complex scenarios...
Aiming at the problem that the existing crowd counting methods cannot achieve accurate crowd counting and map visualization in a large scene, a crowd density estimation and mapping method based on surveillance video and GIS (CDEM-M) is proposed. Firs...
In this paper, we propose a novel congested crowd counting network for crowd density estimation, i.e., the Adaptive Multi-scale Context Aggregation Network (MSCANet). MSCANet efficiently leverages the spatial context information to accomplish crowd d...
Crowd management and monitoring is crucial for maintaining public safety and is an important research topic. Developing a robust crowd monitoring system (CMS) is a challenging task as it involves addressing many key issues such as density variation,...
The detection of crowd density levels and anomalies is a hot topic in video surveillance. Especially in human-centric action and activity-based movements. In some respects, the density level variation is considered an anomaly in the event. Crowd beha...
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. In order to reduce the risk of cross-infection, an advanced pedestrian state sensing method fo...
The number of people in a crowd is crucial information in public safety, intelligent monitoring, traffic management, architectural design, and other fields. At present, the counting accuracy in public spaces remains compromised by some unavoidable si...
Noise levels of anthropogenic origin in urban environments have reached thresholds that pose serious public health and quality of life problems. This paper/work aims to examine these noise levels, the underlying causes of their increase and possible...
For public safety and physical security, currently more than a billion closed-circuit television (CCTV) cameras are in use around the world. Proliferation of artificial intelligence (AI) and machine/deep learning (M/DL) technologies have gained signi...
Recent advances in deep learning-based image processing have enabled significant improvements in multiple computer vision fields, with crowd counting being no exception. Crowd counting is still attracting research interest due to its potential useful...
In recent years, with the rise of smart education, crowd counting technology has garnered increasing attention for its applications in educational environments. During epidemic outbreaks, ensuring the accurate monitoring of student density in classro...
Object detection is a computer vision based technique which is used to detect instances of semantic objects of a particular class in digital images and videos. Crowd density analysis is one of the commonly utilized applications of object detection. S...
For the development of intelligent transportation systems, if real-time information on the number of people on buses can be obtained, it will not only help transport operators to schedule buses but also improve the convenience for passengers to sched...
In order to solve the problem of intelligent management of cattle numbers in the pasture, a dataset of cattle density estimation was established, and a multi-scale residual cattle density estimation network was proposed to solve the problems of uneve...
Interactive movements of bees facilitate the division and organization of collective tasks, notably when they need to face internal or external environmental challenges. Here, we present a Bayesian and computational approach to track the movement of...
This paper attacks the two challenging problems of image-based crowd counting, that is, scale variation and complex background. To that end, we present a novel crowd counting method, called the Scale and Background aware Asymmetric Bilateral Network...
Automated crowd counting is a crucial aspect of surveillance, especially in the context of mass events attended by large populations. Traditional methods of manually counting the people attending an event are error-prone, necessitating the developmen...
Crowd counting is a challenging task dealing with the variation of an object scale and a crowd density. Existing works have emphasized on skip connections by integrating shallower layers with deeper layers, where each layer extracts features in a dif...
The urban open public spaces are the areas where people tend to gather together, which may lead to great crowd-gathering risk. This paper proposes a new method to assess the rank and spatial distribution of crowd-gathering risk in open public spaces...
In this paper, we propose a navigation approach for smartphones that enables visitors of major events to avoid crowded areas or narrow streets and to navigate out of dense crowds quickly. Two types of sensor data are integrated. Real-time optical ima...
Counting people in crowd scenarios is extensively conducted in drone inspections, video surveillance, and public safety applications. Today, crowd count algorithms with supervised learning have improved significantly, but with a reliance on a large a...
Traditional object counting systems use object detection methods to count objects. However, when objects are small, crowded, and dense, object detection may fail, leading to inaccuracies in counting. To address this issue, we propose a crowded object...
Automatic crowd detection in aerial images is certainly a useful source of information to prevent crowd disasters in large complex scenarios of mass events. A number of publications employ regression-based methods for crowd counting and crowd density...
This paper aims to utilize the deep learning architecture to break through the limitations of camera perspective, image background, uneven crowd density distribution and pedestrian occlusion to estimate crowd density accurately. In this paper, we pro...
In recent years, the trampling events due to overcrowding have occurred frequently, which leads to the demand for crowd counting under a high-density environment. At present, there are few studies on monitoring crowds in a large-scale crowded environ...
Crowd counting is an important part of crowd analysis and has been widely applied in the field of public safety and commercial management. Although researchers have proposed many crowd counting methods, there is little research on non-uniform populat...
Train stations have increasingly become crowded, necessitating stringent requirements in the design of stations and commuter navigation through these stations. In this study, we explored the use of mobile eye tracking in combination with observation...
Accurate spatiotemporal information on crowds is a necessity for a better management in general and for the mitigation of potential security risks. The large numbers of individuals involved and their mobility, however, make generation of this informa...
Urban footbridges are critical infrastructure increasingly challenged by vibration issues induced by crowd activity. Real-time monitoring of pedestrian dynamics is essential for evaluating structural safety, ensuring pedestrian comfort, and enabling...
Estimating the number of people in highly clustered crowd scenes is an extremely challenging task on account of serious occlusion and non-uniformity distribution in one crowd image. Traditional works on crowd counting take advantage of different CNN...
Knowing an accurate passengers attendance estimation on each metro car contributes to the safely coordination and sorting the crowd-passenger in each metro station. In this work we propose a multi-head Convolutional Neural Network (CNN) architecture...
Crowd monitoring and analysis has become increasingly used for unmanned aerial vehicle applications. From preventing stampede in high concentration crowds to estimating crowd density and to surveilling crowd movements, crowd monitoring and analysis h...
Visual-based object detection and understanding is an important problem in computer vision and signal processing. Due to their advantages of high mobility and easy deployment, unmanned aerial vehicles (UAV) have become a flexible monitoring platform...
Considering the installation cost and coverage, the received signal strength indicator (RSSI)-based indoor positioning system is widely used across the world. However, the indoor positioning performance, due to the interference of wireless signals th...
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. To achieve this, a context...
The study of patterns of crowd flows represents an emerging and expanding research field. The most straightforward and efficient approach to investigate the patterns of crowd flows is to concentrate on traffic flow. However, assessments of simple poi...
Crowd counting has attracted much attention in computer vision owing to its fundamental contribution in public security. But due to occlusions, perspective distortions, scale variations, and background interference it faces a great challenge. In this...
Estimating object counts within a single image or video frame represents a challenging yet pivotal task in the field of computer vision. Its increasing significance arises from its versatile applications across various domains, including public safet...
With the rapid growth of populations worldwide, the need for intelligent crowd management solutions has become increasingly critical. One of the key challenges in this field is accurately assessing and managing pedestrian movement. Traditional crowd...
The platform–train interface (PTI) is considered a complex space where most interactions occur between passengers boarding and alighting. These interactions are critical under crowded conditions, affecting the experience of traveling and theref...
We propose a symmetric method of accurately estimating the number of metro passengers from an individual image. To this end, we developed a network for metro-passenger counting called MPCNet, which provides a data-driven and deep learning method of u...
Continually improving crowd counting neural networks have been developed in recent years. The accuracy of these networks has reached such high levels that further improvement is becoming very difficult. However, this high accuracy lacks deeper semant...
Simultaneous localization and mapping (SLAM) techniques can be used to navigate the visually impaired, but the development of robust SLAM solutions for crowded spaces is limited by the lack of realistic datasets. To address this, we introduce InCrowd...
Pedestrian panic behavior is a primary cause of overcrowding and stampede accidents in public micro-road network areas with high pedestrian density. However, reliably detecting such behaviors remains challenging due to their inherent complexity, vari...
The outbreak of COVID-19 has resulted in many different policies being adopted across the world to reduce the spread of the virus. These policies include wearing surgical masks, hand hygiene practices, increased social distancing and full country-wid...
Crowd counting, which is widely used in disaster management, traffic monitoring, and other fields of urban security, is a challenging task that is attracting increasing interest from researchers. For better accuracy, most methods have attempted to ha...
Millimeter-wave-based extended target tracking has attracted extensive interest recently because of its privacy, high precision, and low cost. This paper concentrated on crowded indoor situations and presents a novel method for group tracking. First,...
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