Fast Background Subtraction Based on a Multilayer Codebook Model for Moving Object Detection

Guo, Jing-Ming; Hsia, Chih-Hsien; Liu, Yun-Fu; Shih, Min‐Hsiung; Chang, Chao‐Yuan; Wu, Jing-Yu

doi:10.1109/tcsvt.2013.2269011

Cited by 99 publications

(35 citation statements)

References 15 publications

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“…To extract backgrounds from moving points, several methods are using advanced statistic models (Huang and Chen, 2013a;2013b;Cheng et al, 2015;Guo et al, 2013). However, others proposed using neural networks or outlier detection models (Zhou et al, 2012;Huang and Do, 2014).…”

Section: Adaptive Background Subtraction Model (Absm)mentioning

confidence: 99%

Detection of a Moving Car Based on Invariant Moments

Rahma¹,

Basil²

2018

Journal of Computer Science

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This paper presents a proposed technique based on Invariant Moments to detect moving cars in front of Autonomous Cars in order to increase safety and reduce road accidents, thus saving lives, which are one of the most important matters in the context of Autonomous Cars. Object detection from a sequence of images is preparatory step but a crucial mission for computer vision applications in information extraction. In this work, a car database was constructed and used in background subtraction to define Region of Interest, then applying the invariant moments for RoI and dependence on the information of the database that was constructed to detect and recognize the moving car. The technique was tested and the experimental results showed that it could detect the moving objects successfully with detection rate over 87% and precision 97% and FoM 91%.

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Section: Adaptive Background Subtraction Model (Absm)mentioning

confidence: 99%

Detection of a Moving Car Based on Invariant Moments

Rahma¹,

Basil²

2018

Journal of Computer Science

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“…By completing the detection process, a tracking algorithm based on Kalman filter would be applied to ensure that each vehicle would be kept tracked [9,10]. To ensure the process of tracking vehicles regardless the change of position, speed and acceleration, we measure the distance of centroids of each vehicle blob that calculated in two consecutive frames [11,12]. If two centroids (x1, y1) and (x2, y2) of two vehicle blobs, then distance would be calculated as follow:…”

Section: Techniques Of Detecting and Tracking Vehiclesmentioning

confidence: 99%

Traffic Video Enhancement based Vehicle Correct Tracked Methodology

Ata¹,

El-Darieby²,

Elnaby³

et al. 2017

IJIGSP

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Abstract-In this paper, an enhancement based traffic video has been proposed in the state of the art of computer vision. The main target is to develop a decision making criteria for removing the most probable video degradations. Such traffic video degradations would have an adverse impact on the transportation system. In order to establish the appropriate analysis, three types of video degradations have been added to the test video; salt and pepper noise, Gaussian noise, and speckle noise, we have simulated rainy, fog, and darkness conditions for the traffic video. First of all, back ground subtraction and Kalman filter techniques have been used for detecting and tracking vehicles respectively. By using such algorithms, it would be easily to estimate average number of assigned tracks which express the efficacy of correct detection and prediction of vehicles in each frame. Furthermore, video degradations would be applied in order to studying its effect on the average number of assigned tracks which would be deviated than noiseless video. Spatial filtering system has been applied to state the most suitable filter mask which satisfy the least deviation in the average number of assigned tracks. Experimental results show that median filter satisfies the least deviation in all cases of video degradations.

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“…Combining the multilayer block-based strategy and the adaptive feature extraction from blocks of various sizes, the proposed method can remove most of the dynamic background. The pixel-based classification is adopted for refining the results from the block-based background subtraction, which can further classify pixels as foreground, shadows, and highlights [25]. The classic Gaussian mixture model is based on the statistical information of every pixel and it is not robust to light changes.…”

Section: Related Workmentioning

confidence: 99%

Background Subtraction in Dynamic Environment based on Modified Adaptive GMM with TTD for Moving Object Detection

A¹,

Sureshkumar²

2015

Journal of Electrical Engineering and Technology

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-Background subtraction is the first processing stage in video surveillance. It is a general term for a process which aims to separate foreground objects from a background. The goal is to construct and maintain a statistical representation of the scene that the camera sees. The output of background subtraction will be an input to a higher-level process. Background subtraction under dynamic environment in the video sequences is one such complex task. It is an important research topic in image analysis and computer vision domains. This work deals background modeling based on modified adaptive Gaussian mixture model (GMM) with three temporal differencing (TTD) method in dynamic environment. The results of background subtraction on several sequences in various testing environments show that the proposed method is efficient and robust for the dynamic environment and achieves good accuracy.

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Fast Background Subtraction Based on a Multilayer Codebook Model for Moving Object Detection

Cited by 99 publications

References 15 publications

Detection of a Moving Car Based on Invariant Moments

Detection of a Moving Car Based on Invariant Moments

Traffic Video Enhancement based Vehicle Correct Tracked Methodology

Background Subtraction in Dynamic Environment based on Modified Adaptive GMM with TTD for Moving Object Detection

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