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Crowd analysis in non-static cameras using feature tracking and multi-person density
2014
2014 IEEE International Conference on Image Processing (ICIP)
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We propose a new methodology for crowd analysis by introducing the concept of Multi-Person Density. ...
This increases the robustness of separating crowd features from background and thus opens a whole new field for application of these techniques in non-static CCTV cameras. ...
Acknowledgment The research leading to these results has received funding from the European Community's FP7 under grant agreement number 261743 (VideoSense) and number 261776 (MO-SAIC). ...
doi:10.1109/icip.2014.7026219
dblp:conf/icip/SenstEKS14
fatcat:qsuv4g4xjvhppkosz67caylle4
A Literature Review on Video Analytics of Crowded Scenes
[chapter]
2013
Intelligent Multimedia Surveillance
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This chapter presents a review and systematic comparison of the state of the art on crowd video analysis. ...
The rationale of our review is justified by a recent increase in intelligent video surveillance algorithms capable of analysing automatically visual streams of very crowded and cluttered scenes, such as ...
The static cameras are used to provide a global view of the interested persons when the PTZ cameras are used for face recognition of people. ...
doi:10.1007/978-3-642-41512-8_2
fatcat:m45yawffhzgypcn6nsjicrvhkm
Spatio-temporal crowd density model in a human detection and tracking framework
2015
Signal processing. Image communication
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Recently significant progress has been made in the field of person detection and tracking. ...
In this paper, we present a method to enhance human detection and tracking in crowded scenes. ...
Acknowledgement This work was conducted in the framework of the EC funded Network of Excellence VideoSense. ...
doi:10.1016/j.image.2014.11.006
fatcat:564kbvagwfc4nock2mnsx5dnoi
Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey
[article]
2019
arXiv
pre-print
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static camera. ...
We then summarize the important contributions made during last six years on anomaly detection primarily focusing on features, underlying techniques, applied scenarios and types of anomalies using single ...
: Feature studies, learning methods; Multi-camera activity analysis: Correspondence free methods, activity models, human action recognition; Cooperative video surveillance using active and static cameras ...
arXiv:1901.08292v1
fatcat:qehtkb2imfbmpfahkgsjrx7544
Recent trends in crowd analysis: A review
2021
Machine Learning with Applications
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In this review, we explore various studies related to crowd analysis. Crowd analysis is commonly broken down into two major branches: crowd statistics and crowd behavior analysis. ...
When crowd statistics determines the Level Of Service (LoS) of a crowded scene, crowd behavior analysis describes the motion patterns and the activities that are observed in a scene. ...
Although their method can be used in real-time in a multi-cameras setup, the use of RGBD cameras is not enough widespread to make their algorithm applicable in every situation. ...
doi:10.1016/j.mlwa.2021.100023
fatcat:kc5skiri4rho7bmnn62yaiybru
Semi-supervised intelligent surveillance system for secure environments
2010
2010 IEEE International Symposium on Industrial Electronics
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Modules are proposed to perform critical surveillance tasks including: the management and calibration of cameras within a multi-camera network; tracking of objects across multiple views; recognition of ...
people utilising biometrics and in particular softbiometrics; the monitoring of crowds; and activity recognition. ...
ACKNOWLEDGMENT This research was supported by the Australian Government Department of the Prime Minister and Cabinet. ...
doi:10.1109/isie.2010.5636922
fatcat:hsuepfx5evbmvir5zv2726jo3e
A Multi-Camera Tracker for Monitoring Pedestrians in Enclosed Environments
2020
2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)
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We assess the performance of the multi-camera tracker in a case study, tracking customers in a food and speciality market hall. ...
Our multi-camera tracker tracks customers' walking between the stalls in the market. The information is useful for market management, visitor safety, and other potential application areas. ...
INTRODUCTION Computer vision based tracking is gaining importance. For example, it can be used for pedestrian counting and density estimation, for flow analysis, and for event recognition. ...
doi:10.1109/ictai50040.2020.00134
fatcat:lflifojhs5a4pj5sx6llnmp6ie
Comparing Visual Feature Coding for Learning Disjoint Camera Dependencies
2012
Procedings of the British Machine Vision Conference 2012
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Accurate inter-camera dependency estimation across nonoverlapping camera views is non-trivial especially in crowded scenes where inter-object occlusion can be severe and frequent, and when the degree of ...
We show comparative experiments to demonstrate the superiority of robust feature coding for learning inter-camera dependencies using benchmark multi-camera datasets of crowded public scenes. ...
Discovering these timedelayed dependencies or spatio-temporal correlations is of great benefits to many real-world problems such as topology inference [23, 31] , multi-camera tracking [10, 11] , person ...
doi:10.5244/c.26.94
dblp:conf/bmvc/ZhuGL12
fatcat:od3kitt6arh65lwgsnujhbqfie
Enhancing human detection using crowd density measures and an adaptive correction filter
2013
2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance
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In this paper we present a method of improving a human detector by means of crowd density information. ...
We compute crowd density maps in order to estimate the spatial distribution of people in the scene and show how it is possible to enhance the detection results of a state-of-the-art human detector by this ...
Kernel density estimation After generating feature tracks to filter out static points, we define the crowd density map as a kernel density estimate based on the positions of local features. ...
doi:10.1109/avss.2013.6636610
dblp:conf/avss/EiseleinFKSD13
fatcat:gd43l23ww5elhptcd4btahsbkq
Multi-Person Tracking and Crowd Behavior Detection via Particles Gradient Motion Descriptor and Improved Entropy Classifier
2021
Entropy
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After that, the location of all the human silhouettes is fixed and, using the Jaccard similarity index and normalized cross-correlation as a cost function, multi-person tracking is performed. ...
In this paper, we describe our approach to these issues by presenting a novel Particles Force Model for multi-person tracking, a vigorous fusion of global and local descriptors, along with a robust improved ...
The challenges included the S1 dataset for counting persons in a low-density crowd, the S2 dataset for detecting and tracking persons in medium-density crowds, and the S3 dataset for tracking and estimating ...
doi:10.3390/e23050628
pmid:34069994
fatcat:nutz3z7tqvffznautfk23iw3hu
Security and Surveillance
[chapter]
2011
Visual Analysis of Humans
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Substantial efforts have been made towards understanding static images of individual objects and the corresponding processes in the human visual system. ...
A significant application of video analysis and understanding is intelligent surveillance, which aims to interpret automatically human activity and detect unusual events that could pose a threat to public ...
Crowded scene analysis can be categorised into three main problems: (1) crowd density estimation and people counting, (2) tracking in crowd, and (3) behaviour recognition in crowd. ...
doi:10.1007/978-0-85729-997-0_23
fatcat:lodvka4hu5debd7wiovexhca3u
Pedestrian Crowd Detection and Segmentation using Multi-Source Feature Descriptors
2020
International Journal of Advanced Computer Science and Applications
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We evaluate our proposed framework using challenging images with varying crowd densities, camera viewpoints and pedestrian appearances. ...
Crowd detection and segmentation serve as preprocessing step in most crowd analysis applications, for example, crowd tracking, behavior understanding and anomaly detection. ...
The density in images varies from 94 person / image to 4543 persons/image. We randomly divide the data set into training and testing samples using the same convention used in [16] . ...
doi:10.14569/ijacsa.2020.0110187
fatcat:pwm7cmrilza45ftq5dzmvaeqim
SOMPT22: A Surveillance Oriented Multi-Pedestrian Tracking Dataset
[article]
2022
arXiv
pre-print
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For this purpose, we introduce SOMPT22 dataset; a new set for multi person tracking with annotated short videos captured from static cameras located on poles with 6-8 meters in height positioned for city ...
Multi-object tracking (MOT) has been dominated by the use of track by detection approaches due to the success of convolutional neural networks (CNNs) on detection in the last decade. ...
As can be seen in Table 1 , Crowd-Human is a recent person detection dataset with a large volume and density of images. CenterTrack was pre-trained on CrowdHuman dataset [31] by us. ...
arXiv:2208.02580v1
fatcat:dswfyym3nzh7hjbmq2gems7kfi
Density-aware person detection and tracking in crowds
2011
2011 International Conference on Computer Vision
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We address the problem of person detection and tracking in crowded video scenes. ...
, high person densities and significant variation in people's appearance. ...
We thank Pierre Bernas, Philippe Drabczuk, and Guillaume Ne from Evitech for the helpful discussions and the testing videos. ...
doi:10.1109/iccv.2011.6126526
dblp:conf/iccv/RodriguezLSA11
fatcat:shybmxvgeve2regzn5okrnvhsy
Comprehensive Analysis Of Crowd Behavior Techniques: A Thorough Exploration
2022
International Journal of Computing and Digital Systems
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Vision-based crowd behavior analysis methods can be divided into three categories, namely, people counting, people tracking and identification of crowd anomalies. ...
In recent years, one of the most important issues for public security is "Automated analysis of a crowd behavior" using surveillance videos. ...
Uses a Kalman filter for new location prediction, not appropriate for long-term tracking. Fernando et al. [36] Deep Learning Overcomes occlusions and noisy detections in a multi-person environment. ...
doi:10.12785/ijcds/110181
fatcat:v2va6j7g2jes7d6jc7eobktfoa
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