A Computationally Economic Novel Approach for Real-Time Moving Multi-vehicle Detection and Tracking toward Efficient Traffic Surveillance

Hadi, Raad Ahmed; George, Loay E.; Mohammed, Mamon J.

doi:10.1007/s13369-016-2351-8

Cited by 30 publications

(14 citation statements)

References 63 publications

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“…Some pre-processing operations might be taken, including framing the video or changing the color space. Post-processing also could be done by applying various algorithms to overcome a specific challenge in background extraction process [8]. Illustrates the overview of background extraction stages.…”

Section: Background Extraction Stagesmentioning

confidence: 99%

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

Yasir

Ali

2021

Int. J. Onl. Eng.

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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.

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Section: Background Extraction Stagesmentioning

confidence: 99%

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

Yasir

Ali

2021

Int. J. Onl. Eng.

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“…Human activities can include highway maintenance (e.g. traffic density estimation, vehicle tracking, detection of dangerous manoeuvres) [19], [20]; tracking people in public places (e.g. airports, train stations, seaports) [21], [22]; monitoring specific people/objects in mass events (e.g.…”

Section: Background Researchmentioning

confidence: 99%

NeuroHSMD: Neuromorphic Hybrid Spiking Motion Detector

Machado¹,

Ferreira²,

Οικονόμου³

et al. 2021

Preprint

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“…In fact, we subtracted the resultant image of Sobel edge detection process from the resultant image of smoothing process by Laplace filter. Finally, the new detection technique which is inspired from [16,17] applied to detect the ear landmarks region. This new technique used four detectors to scan, collect white pixels of ear edges and recognized it.…”

Section: The Proposed Approach Of Ear Biometric Detectionmentioning

confidence: 99%

Automatic human ear detection approach using modified adaptive search window technique

2021

Self Cite

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The human ear biometric recognition plays an important role in the forensics specialty and has significant impact for biometrician scientists and researchers. Actually, many ear recognition researches showed promised results, but some issues such as manual detection process, efficiency and robustness aren't attained a certain level of maturity. Therefore, the enhancement developing approaches still continuous to achieve limited successes. We propose an efficient, reliable and simple automatic human ear detection approach. This approach implement two stages: preprocessing and ear landmarks detection. We utilized the image contrast, Laplace filter and Gaussian blurring techniques to made enhancement on all images (increasing the contrast, reduce the noisy and smoothing processes). After that, we highlighted the ear edges by using the Sobel edge detector and determining the only white pixels of ear edges by applying the image substation method. The improvement focused on using the modified adaptive search window (ASW) to detect the ear region. Furthermore, our approach is tested on Indian Institute of Technology (IIT) Delhi standard ear biometric public dataset. Experimental results presented a well average detection rate 96% for 493 image samples from 125 persons and computational time almost ≈ 0.485 seconds which is evaluated with other previous works.

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A Computationally Economic Novel Approach for Real-Time Moving Multi-vehicle Detection and Tracking toward Efficient Traffic Surveillance

Cited by 30 publications

References 63 publications

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

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

NeuroHSMD: Neuromorphic Hybrid Spiking Motion Detector

Automatic human ear detection approach using modified adaptive search window technique

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