Moving object detection using self adaptive Gaussian Mixture Model for real time applications

Ali, Syed Tariq; Goyal, Kalpana; Singhai, Jyoti

doi:10.1109/rise.2017.8378144

Cited by 14 publications

(12 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Vehicle types were identified using the Gaussian Mixture Model (GMM). GMM is a robust method for dynamic backgrounds [11]. The advantage of the GMM algorithm can show multi-mode changes in pixel values, account lighting changes, repetitive space movements and scene changes in real time [12], [13].…”

Section: Proposed Methodsmentioning

confidence: 99%

Tracking of Vehicle Tax Violations Using Vehicle Type and Plate Number Identification

Jaya¹,

Zainuddin²,

Syarif³

2019

Proceedings of the 1st International Conference on Science and Technology, ICOST 2019, 2-3 May, Makassar, Indonesia

View full text Add to dashboard Cite

This paper describes tracking violations of vehicle tax using the identification of type and number plates on vehicles. The case study of this research is in Sulawesi Selatan (Indonesia). The Gaussian Mixture Model (GMM) algorithm is used to detect vehicle types and the Automatic Number Plate Recognition (ANPR) algorithm for detecting vehicle license numbers. The proposed system uses a digital camera with a camera height of 250 cm and a camera tilt angle of 55 degrees. The proposed system design is the stages of preprocessing, feature extraction, feature selection, model selection, and database. For the introduction of types of vehicles are classified based on predetermined ROI while the introduction of vehicle numbers is trained using k-Nearest Neighbor (KNN). The results of the proposed system accuracy are 91% for vehicle type detection and 70% for vehicle license number detection.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

Tracking of Vehicle Tax Violations Using Vehicle Type and Plate Number Identification

Jaya¹,

Zainuddin²,

Syarif³

2019

Proceedings of the 1st International Conference on Science and Technology, ICOST 2019, 2-3 May, Makassar, Indonesia

View full text Add to dashboard Cite

show abstract

“…Improved Gaussian background modeling (GBM) is another enhanced model in which wavelet denoising is applied on foreground object, this model achieved a better performance in respect to shadow and lighting changes challenges [26]. On the other hand a Self-Adaptive Gaussian Mixture Mode improved the speed performance of GMM four times by using a block of pixels instead of considering one pixel value [27].…”

Section: Mathematical Modelsmentioning

confidence: 99%

Review on Real Time Background Extraction: Models, Applications, Environments, Challenges and Evaluation Approaches

Yasir

Ali

2021

Int. J. Onl. Eng.

View full text Add to dashboard Cite

In the computer vision, background extraction is a promising technique. It is characterized by being applied in many different real time applications in diverse environments and with variety of challenges. Background extraction is the most popular technique employed in the domain of detecting moving foreground objects taken by stationary surveillance cameras. Achieving high performance is required with many perspectives and demands. Choosing the suitable background extraction model plays the major role in affecting the performance matrices of time, memory, and accuracy.In this article we present an extensive review on background extraction in which we attempt to cover all the related topics. We list the four process stages of background extraction and we consider several well-known models starting with the conventional models and ending up with the state-of-the art models. This review also focuses on the model environments whether it is human activities, Nature or sport environments and illuminates on some of the real time applications where background extraction method is adopted. Many challenges are addressed in respect to environment, camera, foreground objects, background, and computation time. In addition, this article provides handy tables containing different common datasets and libraries used in the field of background extraction experiments. Eventually, we illustrate the performance evaluation with a table of the set performance metrics to measure the robustness of the background extraction model against other models in terms of time, accurate performance and required memory.

show abstract

“…The moving object detection by Ali et al (2017) uses a standard GMM (Gaussian Mixture Model) and models the intensity values of a block instead of a pixel (Li et al, 2017). They employed a dynamic learning rate to overcome the trade-off in detection accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…The moving objects are detected in the first module by using a background subtraction algorithm based on GMM. The reason for using GMM is its robustness to background variations (Ali et al, 2017). Further, the process carried out segmentation and morphological operations and the location of moving objects in next frame was predicted using a Kalman filter from object's velocity or acceleration (Kalman, 1960).…”

Section: Current Best Solution Processmentioning

confidence: 99%

An Improved Object Detection Technique for Hazard Avoidance Systems

Deol¹,

Prasad²,

Alsadoon³

et al. 2018

American Journal of Applied Sciences

View full text Add to dashboard Cite

Hazard detection and avoidance at construction sites working with heavy equipment and moving vehicles is one of the biggest issues in modern surveillance. Background subtraction using a Gaussian Mixture Model (GMM) is widely utilized for identification of moving objects with most existing methods leading to improvements but lacking accuracy of object detection. This paper aims to improve accuracy and processing time for object detection. The proposed algorithm consists of a correlation coefficient to reduce the existing geometric error and provide more accurate detection of moving objects by comparing foreground and background pixels in every frame. A Kalman filter is used for keeping track of the object. The results demonstrate that the proposed algorithm outperforms existing applications in terms of accuracy of object detection. On this basis, it is recommended that object detection with a correlation coefficient of background and foreground pixels of objects can be used for hazard detection in real-time monitoring systems such as traffic monitoring and detection and tracking of humans.

show abstract

Moving object detection using self adaptive Gaussian Mixture Model for real time applications

Cited by 14 publications

References 11 publications

Tracking of Vehicle Tax Violations Using Vehicle Type and Plate Number Identification

Tracking of Vehicle Tax Violations Using Vehicle Type and Plate Number Identification

Review on Real Time Background Extraction: Models, Applications, Environments, Challenges and Evaluation Approaches

An Improved Object Detection Technique for Hazard Avoidance Systems

Contact Info

Product

Resources

About