crowd counting


Feature mining for localised crowd counting .
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This paper presents a multi-output regression model for crowd counting in public scenes. Existing counting by regression methods either learn a single model for global counting , or train a large number of separate regressors for localised density estimation. In contrast, our

Single-image crowd counting via multi-column convolutional neural network
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This paper aims to develop a method than can accurately estimate the crowd count from an individual image with arbitrary crowd density and arbitrary perspective. To this end, we have proposed a simple but effective Multi-column Convolutional Neural Network (MCNN)

Switching convolutional neural network for crowd counting
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We propose a novel crowd counting model that maps a given crowd scene to its density. Crowd analysis is compounded by myriad of factors like inter-occlusion between people due to extreme crowding, high similarity of appearance between people and background

Real-time monitoring for crowd counting using video surveillance and GIS
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In public venues, crowd size is a key indicator of crowd safety and stability. Monitor the people number and crowd density levels are important scientific research topics. In this paper, we present a framework that will enable real-time crowd counting and spatial

Spatiotemporal modeling for crowd counting in videos
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Region of Interest (ROI) crowd counting can be formulated as a regression problem of learning a mapping from an image or a video frame to a crowd density map. Recently, convolutional neural network (CNN) models have achieved promising results for crowd

Freecount: Device-free crowd counting with commodity wifi
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In the era of Internet of Things, crowd counting , which estimates the number of people within a region, becomes the underpinning for many emerging applications, such as occupancy estimation in smart building and queuing management and product placement in shopping

Body structure aware deep crowd counting
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Crowd counting is a challenging task, mainly due to the severe occlusions among dense crowds. This work aims to take a broader view to address crowd counting from the perspective of semantic modelling. In essence, crowd counting is a task of pedestrian

Counting of people in the extremely dense crowd using genetic algorithm and blobs counting
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In this paper, we have proposed a framework to count the moving person in the video automatically in a very dense crowd situation. Median filter is used to segment the foreground from the background and blob analysis is done to count the people in the current

Real time and scene invariant crowd counting : Across a line or inside a region
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In this paper, we propose a blob-based method of crowd counting across a line of interest (LOI), which can be further extended to counting inside a region of interest (ROI). Firstly, we detect moving blobs in which low-level features are extracted and grouped. Since features

People counting in extremely dense crowd using blob size optimization
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Estimating Crowd density and counting people is an important factor in crowd management. The increase of number of people in small areas may create problems like physical injury and fatalities. Hence early detection of the crowd can avoid these problems. Counting of the

Towards View Invariant Person Counting and Crowd Density Estimation for Remote Vision-Based Services
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Crowd monitoring in mass events is a highly important technology to support the safety of event attending persons. Proposed methods are often limited to one specific viewing condition and have to be retrained or even redesigned if the viewing angle is changing

Crowd Safety: A Real Time System For Counting People
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Man-made disasters are the outcome of lack of awareness, lack of sensitivity towards the safety measures to be taken to prevent unforeseen accidents. Large crowds always invite accidents if preventive measures are not taken with proper planning. When the number of

CountMe!-Low Cost Crowd Counting using Audio Tones
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With mobile devices becoming ubiquitous, collaborative applications have become increasingly pervasive. In these applications, there is a strong need to obtain a count of the number of mobile devices present in an area, as it closely approximates the size of the

A Distributed Protocol for Crowd Counting in Urban Environments
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Monitoring, control, and estimation of spontaneous crowd formations in cities, eg, during open-air festivals or rush hour, are necessary actions to be taken by city administration. The most common way to implement these measures is via installation of observation cameras

Supplementary Material: Switching Convolutional Neural Network for Crowd Counting
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Differential training on the CNN regressors R1 through R3 generates a multichotomy that minimizes the predicted count by choosing the best regressor for a given crowd scene patch. However, the trained switch is not ideal and the manifold separating the space of patches is

Counting People in a Crowd Using Viola-Jones Algorithm
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Counting the number of people in crowded areas such as the Masaa of Al-Masjid Al-Haram has become a necessity. This is due to the increasing number of people performing Hajj or Umrah. In this paper, we propose a crowd counting system for the Masaa using image

Iterative Crowd Counting
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In this work, we tackle the problem of crowd counting in images. We present a Convolutional Neural Network (CNN) based density estimation approach to solve this problem. Predicting a high resolution density map in one go is a challenging task. Hence, we present a two

Counting in High Density Crowd Videos
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We propose a method for getting an estimate count of people in very high-dense crowd videos by extending a static crowd count method. Due to the challenging problem of perspective, occlusion, clutter, and low resolution counting by detection is not possible

Scale Aggregation Network for Accurate and Efficient Crowd Counting
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In this paper, we propose a novel encoder-decoder network, called Scale Aggregation Network (SANet), for accurate and efficient crowd counting . The encoder extracts multi-scale features with scale aggregation modules and the decoder generates high-resolution density

CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting
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Estimating crowd count in densely crowded scenes is an extremely challenging task due to non-uniform scale variations. In this paper, we propose a novel end-toend cascaded network of CNNs to jointly learn crowd count classification and density map estimation



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