Moving object detection in real-time visual surveillance using background subtraction technique

Yadav, Dileep Kumar; Sharma, Lavanya; Bharti, Sunil Kumar

doi:10.1109/his.2014.7086176

Cited by 16 publications

(17 citation statements)

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“…Su and Hu (2004) developed a GMM based statistical framework to localise moving foreground and resolve the issue of high dynamic nature of background. Yadav et al (2014) developed a fast adaptive method that is capable enough to handle the dynamic background. Ng and Delp (2011) method handles the gradual illumination variation very well but fails to handle the sudden illumination change.…”

Section: Related Workmentioning

confidence: 99%

“…The performance of the proposed work is evaluated and compared with considered state-of-the-art methods using some metrics (Yadav et al, 2014;Yadav and Singh, 2015;Yilmaz et al, 2004) as given below. Here, TP represents valid object pixel which is correctly classified as part of the foreground.…”

Section: Performance Analysismentioning

confidence: 99%

“…For example, Yahoo Labs (2010), the proposed method can also enable a coastguard to monitor large stretches of waterway and coastline for detection of fishing, smuggling, or illegal intrusion etc. Some other open problem oriented video surveillance applications are given below (Hu et al, 2013;Yilmaz et al, 2004;Yadav et al, 2015;Sharma et al, 2016).…”

Section: Real-time Applications For Visual Surveillancementioning

confidence: 99%

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Adaptive background modelling technique for moving object detection in video under dynamic environment

Yadav¹,

Singh²

2019

IJSTDS

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This work proposes a novel method for detection of motion based object having dynamic scenario in the background. The suggested scheme has a strong potential for real-time applications especially for rafting, river, sea-beach, swimming pools, ponds, etc. Apart from these, this work is very beneficial for surveillance of border, tunnel, traffic in the sea, forest, restricted zones, deep zones, etc. This work develops a statistical p based background subtraction method and implemented in three stages. In the first stage, a background model is developed using few initial frames. In the second stage, this work classifies the foreground using the difference frame and the appropriate threshold value. An automatic threshold value is generated at run-time and updated iteratively. It also reduces the problem of using a constant threshold. In the third stage, morphological filters and connected component based region filtering technique is applied to enhance the detection quality. The extensive experimental result shows more accurate results of proposed method. It also demonstrates better performance against considered state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Section: Performance Analysismentioning

confidence: 99%

Section: Real-time Applications For Visual Surveillancementioning

confidence: 99%

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Adaptive background modelling technique for moving object detection in video under dynamic environment

Yadav¹,

Singh²

2019

IJSTDS

Self Cite

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“…Here, all the experiments have been performed on gray-scale frame sequences. Real-time problematic scenarios (Change Detection dataset [13] with frame size 320 × 240, and Wallflower [14] with frame size 160 × 120) have been used for experimental analysis. The proposed experiment is carried out on Windows 8.1 operating system over Intel (R) Core (TM) i5 processor with CPU 1.70 GHz speed and 4 GB RAM.…”

Section: Experimental Setup and Analysismentioning

confidence: 99%

“…The precision, recall and f-measure [14] are given as: The f-measure is used to measure the detection quality. The true positive rate (TPR) depicts proportion of moving object's pixel whereas false positive rate (FPR) depicts proportion of static background pixel which was erroneously classified as part of foreground.…”

Section: Quantitative Analysismentioning

confidence: 99%

Moving Object Detection for Visual Surveillance Using Quasi-euclidian Distance

Yadav¹,

Singh²

2015

Advances in Intelligent Systems and Computing

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Moving object detection is a fundamental step for visual surveillance system, other image processing, and computer vision applications. The most popular and common technique for moving foreground detection is background subtraction. In dynamic background, Gaussian Mixture Model performs better for object detection. In this work, a GMM-based background model is developed. This work proposes a quasi-euclidian distance measure in order to measure the variation in terms of distance, between modeled frame and test frame. To classify the pixel, this distance is compared with a suitable threshold. The connected component and blob labeling has been used to improve the model with a threshold. Morphological filter is used to improve the foreground information. The experimental study shows that the proposed work performs better in comparison to considered state-of-the-art methods in term precision, recall, and f-measure.

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