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Enhancing human detection using crowd density measures and an adaptive correction filter

Volker Eiselein, Hajer Fradi, Ivo Keller, Thomas Sikora, Jean-Luc Dugelay
2013 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance  
The proposed method applies a self-adaptive, dynamic parametrization and as an additional contribution uses scene-adaptive learning of the human aspect ratio in order to reduce false positive detections  ...  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  ...  Apart from introducing crowd density information, in Section 4.2 we present an adaptive filtering step which additionally enhances the results.  ... 
doi:10.1109/avss.2013.6636610 dblp:conf/avss/EiseleinFKSD13 fatcat:gd43l23ww5elhptcd4btahsbkq

Spatio-temporal crowd density model in a human detection and tracking framework

Hajer Fradi, Volker Eiselein, Jean-Luc Dugelay, Ivo Keller, Thomas Sikora
2015 Signal processing. Image communication  
Our proposed approach applies a scene-adaptive dynamic parametrization using this crowd density measure.  ...  In this paper, we present a method to enhance human detection and tracking in crowded scenes.  ...  Figure 6 : 6 incorporating local crowd density and geometrical correction filters in the nonmaximum suppression step and used the resulting detections for tracking.  ... 
doi:10.1016/j.image.2014.11.006 fatcat:564kbvagwfc4nock2mnsx5dnoi

Contextualized Privacy Filters in Video Surveillance Using Crowd Density Maps

Hajer Fradi, Andrea Melle, Jean-Luc Dugelay
2013 2013 IEEE International Symposium on Multimedia  
This additional information cue consists of modeling time-varying dynamics of the crowd density using local features as an observation of a probabilistic crowd function.  ...  In this paper, we specifically focus on the dependency between privacy preservation and crowd density.  ...  Using an additional RoIs detection step, we adapt the degree of data obfuscation for privacy according to the crowd level.  ... 
doi:10.1109/ism.2013.23 dblp:conf/ism/FradiMD13 fatcat:vs2p6gqcivdkvf22ac2ffptory

KALMAN FILTER BASED FEATURE ANALYSIS FOR TRACKING PEOPLE FROM AIRBORNE IMAGES

B. Sirmacek, P. Reinartz
2012 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Using an adaptive kernel density estimation method, we estimate the corresponding pdf. First, we use estimated pdf to detect boundaries of dense crowds.  ...  After that, using background information of dense crowds and previously extracted local features, we detect other people in non-crowd regions automatically for each image in the sequence.  ...  The filtering process consists of two main steps; time update (prediction) step, and measurement update (correction) step.  ... 
doi:10.5194/isprsarchives-xxxviii-4-w19-303-2011 fatcat:v3govmgmbjdi3hubptvvgft3g4

Adaptive colour restoration and detail retention for image enhancement

Kangjian He, Dapeng Tao, Dan Xu
2021 IET Image Processing  
To improve the imaging quality and visual effect, an adaptive colour restoration and detail retention-based method is proposed for image enhancement.  ...  Improving imaging quality is the key to improve the performance of crowd analysis, density estimation, target recognition, segmentation, and detection in computer vision tasks.  ...  Crowd density analysis, detection and intelligent transportation are popular research topics in the field of public safety.  ... 
doi:10.1049/ipr2.12223 fatcat:ynuwvbeeifftfovdya2lakry3m

One Shot Crowd Counting with Deep Scale Adaptive Neural Network

Junfeng Wu, Zhiyang Li, Wenyu Qu, Yizhi Zhou
2019 Electronics  
In this paper, we proposed a new neural network called Deep Scale-Adaptive Convolutional Neural Network (DSA-CNN), which can convert a single crowd image to density map for crowd counting directly.  ...  crowd density accurately.  ...  Crowd density distribution is extremely uneven due to perspective effects, viewpoint changes and crowd density changes. This makes it difficult for us to measure crowd density with one scale.  ... 
doi:10.3390/electronics8060701 fatcat:v2af64louzgdnlxmkfredraj54

Crowd Behavior Analysis using MoDTA approach

Savitha C, Dr. Ramesh. D
2019 Indonesian Journal of Electrical Engineering and Computer Science  
In this paper, we are proposing multi-observational detection and tracking approach (MoDTA) that is based on observational filter.  ...  The MoDTA initially acquires<span> </span>people location in an image, </span>so that is <span>can detect conviction value at pointed locations which generally increases with respect to people density.  ...  The work includes observing the human nature patterns in an over-all continuously changing nature and adapt with the time, rather being static.  ... 
doi:10.11591/ijeecs.v15.i1.pp484-494 fatcat:lbudxmlxmbh7paewo7mcsd22jq

Tracking Pedestrian Heads in Dense Crowd

Ramana Sundararaman, Cedric De Almeida Braga, Eric Marchand, Julien Pettre
2021 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
Increasing crowd density challenges visibility of humans, limiting the scalability of existing pedestrian trackers to higher crowd densities.  ...  Tracking humans in crowded video sequences is an important constituent of visual scene understanding.  ...  Vicky Kalogeiton and Prof. Dr. Bastian Leibe for their insightful feedback. We are also thankful to our annotators for their hard work.  ... 
doi:10.1109/cvpr46437.2021.00386 fatcat:4k4kcmizjvgttl4sqbwrfhj4gy

A Practical and Automated Image-based Framework for TrackingPedestrian Movements from a Video

Halimatul Saadiah Md. Yatim, Abdullah Zawawi Talib, Fazilah Haron
2013 Information Engineering Research Institute procedia  
to acknowledge the support of the Ministry of Higher Education Malaysia for this research under the Fundamental Research Grant Scheme entitled "More Accurate Models for Movements of Pedestrians in Big Crowds  ...  The steps used in the implementation of the framework are adapted from some existing works of other researchers.The techniques used in our approach include: image processing, extracting objects and filtering  ...  In term of detecting trajectories of an object, the object can be a pedestrian [10] , vehicle [11] , human fingertip [12] and many more.  ... 
doi:10.1016/j.ieri.2013.11.026 fatcat:bgq4ll3nabh2dasgjrowabbahi

A Flow Base Bi-path Network for Cross-scene Video Crowd Understanding in Aerial View [article]

Zhiyuan Zhao, Tao Han, Junyu Gao, Qi Wang, Xuelong Li
2020 arXiv   pre-print
Drones shooting can be applied in dynamic traffic monitoring, object detecting and tracking, and other vision tasks.  ...  First, to alleviate the background noise generated in cross-scene testing, a double-stream crowd counting model is proposed, which extracts optical flow and frame difference information as an additional  ...  Performance Measures Researchers use MAE and mean squared error (MSE) to measure the differences between the predicted density map and the ground truth.  ... 
arXiv:2009.13723v1 fatcat:g7zowyhpuvhzrgmzkp43bekon4

Crowd Detection in Still Images Using Combined HOG and SIFT Feature

Machbah Uddin, Hira Lal Gope, Md. Sayeed Iftekhar Yousuf, Dilshad Islam, Mohammad Khairul Islam
2016 Indonesian Journal of Electrical Engineering and Computer Science  
An enhanced system of interest point detection based on gradient orientation information as well as improved feature extraction HOG is used for identifying the human head or face from crowd.  ...  <p>Person detection and tracking in crowd is a challenging task. We detect the head region and based on this head region we can detect people from crowd.  ...  for human head detection and crowd detection.  ... 
doi:10.11591/ijeecs.v4.i2.pp447-458 fatcat:akhphzl76vfqpa7pgq5xotc5eu

Social Density Monitoring Toward Selective Cleaning by Human Support Robot with 3D based Perception System

Anh Vu Le, Balakrishnan Ramalingam, Felix Gomez Braulio, Rajesh Elara Mohan, Tran Hoang Quang Minh, Vinu Sivanantham
2021 IEEE Access  
The surveillance framework adopts the 3D human joints tracking technique and the accumulated asymmetrical Gaussian distribution scheme modeling the human location, size, and direction to quantify human  ...  density.  ...  ACKNOWLEDGMENT The authors would like to thank Jia Yin for insightful comments and discussion.  ... 
doi:10.1109/access.2021.3065125 fatcat:cjppgdpaw5ejji5krra6avm5aq

Intelligent video surveillance: a review through deep learning techniques for crowd analysis

G. Sreenu, M. A. Saleem Durai
2019 Journal of Big Data  
The paper includes a deep rooted survey which starts from object recognition, action recognition, crowd analysis and finally violence detection in a crowd environment.  ...  The main focus of this survey is application of deep learning techniques in detecting the exact count, involved persons and the happened activity in a large crowd at all climate conditions.  ...  Density based person detection and tracking include steps like baseline detector, geometric filtering and tracking using density aware detector.  ... 
doi:10.1186/s40537-019-0212-5 fatcat:mh7d5d5c5zeczf5sdmgwz3claq

Comprehensive Analysis Of Crowd Behavior Techniques: A Thorough Exploration

Safvan Vahora, Krupa Galiya, Harsh Sapariya, Srijyaa Varshney
2022 International Journal of Computing and Digital Systems  
With this perception, we have presented an extensive review of the different methods for crowd counting, crowd tracking, and crowd anomaly detection along with the advantages and challenges associated  ...  PCA, HOG, SIFT, optical flow, GMM, spatiotemporal filter, etc. and the self-learned feature descriptor based approaches which use deep learning models like CNN, RNN, GD-GAN, etc.  ...  DecideNet [54] Deep Learning Adaptive to varying crowd densities.  ... 
doi:10.12785/ijcds/110181 fatcat:v2va6j7g2jes7d6jc7eobktfoa

Scanning the Issue

Azim Eskandarian
2021 IEEE transactions on intelligent transportation systems (Print)  
However, achieving an accurate crowd counting and generating a precise density map are still challenging tasks due to occlusion, perspective distortion, complex backgrounds, and varying scales.  ...  In addition, most of the existing methods focus only on the accuracy of crowd counting without considering the correctness of a density distribution, namely, there are many false negatives and false positives  ...  Scanning the Issue Crowd Density Estimation Using Fusion of Multi-Layer Features X. Ding, F. He, Z. Lin, Y. Wang, H. Guo, and Y.  ... 
doi:10.1109/tits.2021.3098395 fatcat:s7r5jkvwbfe2hntml6gylicktq
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