An adaptive hybrid GMM for multiple human detection in crowd scenario

Karpagavalli, P.; Ramprasad, A. V.

doi:10.1007/s11042-016-3777-4

Cited by 13 publications

(7 citation statements)

References 17 publications

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“…There are few frames in Highway dataset which are associated with shadow due to which AGMM [23], WePBAS [29], and ViBe [27] methods generate more pixels misclassi cations (considering shadow pixels as foreground) and increases more false positive rate (FPR). It is observed that in Highway dataset, RAG [22] shows bad recall when more pixels are misclassi ed due to long shadow. Proposed method is also having capability of detecting and removing shadow pixels which lead to less pixels' misclassi cation and generate less false positive rate values than other methods.…”

Section: Quantitative Resultsmentioning

confidence: 99%

“…Comparison is done on the basis of two stages model. First stage is used to generate binary mask using background subtraction model and compare with ve state of the art methods (i) Temporal Average Filter [5], (ii) Running Average Gaussian (RAG) [22], (iii) Adaptive GMM [23], (iv) WePBAS [29], (v) ViBe [27]. These methods are chosen on the requirement of mapping threshold value for foreground segmentation.…”

Section: Comparisons Of Qualitative Results Of Proposed Background Su...mentioning

confidence: 99%

“…To make it more advance, adaptive GMM with EM method is introduced for background subtraction which is used to predict per-pixel background distributions [21]. Paper [22] proposes hybrid GMM model for static and dynamic background but not able to handle shadows in the image when illumination changes. Another statistical background subtraction model is proposed by paper [23] which is integrated with wavelet transform to detect moving objects in illumination changes but provides pixels misclassi cation when shadow exists.…”

Section: Related Workmentioning

confidence: 99%

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ABGS Segmenter: Pixel wise Adaptive Background Subtraction and Intensity Ratio based Shadow Removal Approach for Moving Object Detection in Video Surveillance Application

Agrawal

Natu

2022

Preprint

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Background subtraction approaches are used to detect moving objects with high recognition rate and less computation time. These methods are facing two challenges such as selection of appropriate threshold value and shadow pixels’ removal for correct foreground detection. In this paper, we solve these challenges by proposing new background subtraction method called ABGS Segmenter which is based on two level adaptive thresholding approach where reference frame is created using mean based thresholding to generate initial value of threshold and accelerate the process of foreground segmentation for remaining frames by adaptively updating the threshold value at pixel level. ABGS Segmenter is also capable to remove shadow pixels by fusing chromaticity based YCbCr color space model with intensity ratio method for improving the percentage of correct pixels’ classification measure. Comprehensive experiments are evaluated on three benchmark datasets (Highway, PET 2006 and SBU) and observed that proposed work achieves better results than existing methods.

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Section: Quantitative Resultsmentioning

confidence: 99%

Section: Comparisons Of Qualitative Results Of Proposed Background Su...mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

ABGS Segmenter: Pixel wise Adaptive Background Subtraction and Intensity Ratio based Shadow Removal Approach for Moving Object Detection in Video Surveillance Application

Agrawal

Natu

2022

Preprint

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“…In [38] an expert crowd control and management system for hajj has been used with three strategies i.e., to address congestion and overcrowded situation using; First In First Out (FIFO), priority queuing and Weighted Round Robin (WRR). An automatic multiple human detection method using hybrid adaptive Gaussian mixture model was introduced in [82] for human detection. The efficiency of proposed method has further evaluated and analyzed by using Receiver Operating/Output Characteristics (ROC), Mean Absolute Error (MAE) and Mean Relative Error (MRE).…”

Section: Discussionmentioning

confidence: 99%

Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review

et al. 2020

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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, irregular distribution of objects, occlusions, pose estimation, etc. Crowd gathering at various places like hospitals, parks, stadiums, airports, cultural and religious points are usually monitored by Close Circuit Television (CCTV) cameras. The drawbacks of CCTV cameras are: limited area coverage, installation problems, movability, high power consumption and constant monitoring by the operators. Therefore, many researchers have turned towards computer vision and machine learning that have overcome these issues by minimizing the need of human involvement. This review is aimed to categorize, analyze as well as provide the latest development and performance evolution in crowd monitoring using different machine learning techniques and methods that are published in journals and conferences over the past five years.

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“…In the first task, this research addresses the problem of human detection and tracking [ 6 , 9 , 12 ] in the surveillance video. Human detection is a two-stage process that involves the localization of an object in the first stage and classification of the localized object in the second stage.…”

Section: Introductionmentioning

confidence: 99%

Monitoring social distancing through human detection for preventing/reducing COVID spread

Ansari

Singh

2021

Int. j. inf. tecnol.

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COVID-19 is a severe epidemic that has put the world in a global crisis. Over 42 Million people are infected, and 1.14 Million deaths are reported worldwide as on Oct 23, 2020. A deeper understanding of the epidemic suggests that a person’s negligence can cause widespread harm that would be difficult to negate. Since no vaccine is yet developed, social distancing must be practiced to detain COVID-19 spread. Therefore, we aim to develop a framework that tracks humans for monitoring the social distancing being practiced. To accomplish this objective of social distance monitoring, an algorithm is developed using object detection method. Here, CNN based object detector is explored to detect human presence. The object detector’s output is used for calculating distances between each pair of humans detected. This approach of social distancing algorithm will red mark the persons who are getting closer than a permissible limit. Experimental results prove that CNN based object detectors with our proposed social distancing algorithm exhibit promising outcomes for monitoring social distancing in public areas.

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An adaptive hybrid GMM for multiple human detection in crowd scenario

Cited by 13 publications

References 17 publications

ABGS Segmenter: Pixel wise Adaptive Background Subtraction and Intensity Ratio based Shadow Removal Approach for Moving Object Detection in Video Surveillance Application

ABGS Segmenter: Pixel wise Adaptive Background Subtraction and Intensity Ratio based Shadow Removal Approach for Moving Object Detection in Video Surveillance Application

Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review

Monitoring social distancing through human detection for preventing/reducing COVID spread

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