Image Processing in Road Traffic Analysis

Atkočiūnas, E.; Blake, Robert A.; Juozapavičius, Algimantas; Kazimianec, Michail

doi:10.15388/na.2005.10.4.15112

Cited by 36 publications

(5 citation statements)

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“…The actual detection area value is obtained by calculating the area of the road that is on the Moh. Toha Toll Road based on Toll Road Geometry Standard [18].…”

Section: A Determine the True Value Of The Actual Detectionmentioning

confidence: 99%

Detection of Traffic Density With Image Processing Using Pin Hole Algorithm

Irawanto

Setianingsih

Irawan

2022

IIUMEJ

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The intelligent traffic monitors are devloped and became more interst in recent years. A detection system in the monitoring traffic system is proposed using different algorithms. Pin Hole Algorithm used to detect the car that passes the road (the studied area). A fixed camera mounted at predetermined point used with known height (of the camera), the intensity of the light, and the visibility of the camera. The classification process is important to know the traffic congestion status. The traffic congestion status will be sent to the server address already provided. In the congestion detection test results were obtained with an accuracy value of 85% using the 64x64 grid division and obtaining good detection results for susceptible light intensity values between 5430 and 41379 LUX with an accuracy value of between 60% and 90%. ABSTRAK: Sejak beberapa tahun ini, sistem pengawasan trafik pintar telah dibina dan terus berkembang luas. Sistem pengesanan dalam sistem trafik pengawasan telah dicadangkan menggunakan pelbagai algoritma. Algoritma lubang pin digunakan bagi mengesan kereta yang melalui jalan (kawasan kajian). Kamera dipasang tetap pada titik tertentu iaitu dengan menyelaras ketinggian kamera, keamatan cahaya, dan kebolehlihatan kamera. Proses klasifikasi sangat penting bagi menentukan status kesesakan trafik. Status kesesakan trafik akan dihantar ke alamat pelayan yang telah disediakan. Nilai ketepatan ujian pengesanan kesesakan yang diperoleh adalah 85% iaitu menggunakan pembahagi grid 64x64 dan dapatan kajian menunjukkan pengesanan yang baik bagi nilai keamatan cahaya antara 5430 dan 41379 LUX dengan nilai ketepatan antara 60% dan 90%.

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“…The actual detection area value is obtained by calculating the area of the road that is on the Moh. Toha Toll Road based on Toll Road Geometry Standard [18].…”

Section: A Determine the True Value Of The Actual Detectionmentioning

confidence: 99%

Detection of Traffic Density With Image Processing Using Pin Hole Algorithm

Irawanto

Setianingsih

Irawan

2022

IIUMEJ

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“…Unfortunately, the device does not work with most motorbikes. Using a passive system such as a camera along with image processing may prove to be more effective at detecting vehicles than the current system [1].…”

Section: Related Workmentioning

confidence: 99%

“…As the lanes in some of the roads are found to be not clear, a threshold based image enhancement is adopted. Many works like Road Traffic Analysis and Traffic Sign Detection were conducted [1], [2], [8], [9]. Here, TPS is applied over the extracted sign image to get features for road signs.…”

Section: Proposed Workmentioning

confidence: 99%

An Image Processing Approach to Detect Lanes, Pot Holes and Recognize Road Signs in Indian Roads

Danti¹,

Kulkarni²,

Hiremath³

2012

IJMO

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“…After the video stream is converted to single frames with corresponding RGB values for each point in that frame, a four step process is undertaken, as now described. Each step has a specific processing algorithm, which builds on past work (Taktak et al 1996;Atkociunas et al 2005), while adding a new contour extraction scheme to perform vehicle classification. Figure 1 will demonstrate the result of each step on a sample image.…”

Section: Image-based Vehicle Detection and Classificationmentioning

confidence: 99%

A Citizen-Centric Health Monitoring Paradigm Using Embedded Self-Locating Wireless Sensor Networks

Kijewski-Correa

Montestruque³

2012

20th Analysis and Computation Specialty Conference

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While integrated health monitoring systems are often presented as a solution to the national infrastructure crisis, this project adopts a unique variation on this theme by actively seeking to remove barriers that have precluded non-expert citizen's direct use of these systems. Often the expense and need for expert involvement in most structural health monitoring programs means that the vast majority of bridges, including those that are structurally deficient and in greatest need of assessment, cannot benefit from these efforts. By recognizing that our target end users have limited budgets and large inventories of bridges, this project delivers to these end users a rapidly re-deployable, self-organizing wireless sensor network. However, by providing end users full freedom to deploy these units at will, this project must do something all competing efforts have not: operate without full knowledge of where sensors are placed not only in detecting damage but also in relaying that information through a wireless network. This paper focuses specifically on how this wireless network will be activated in a restricted fashion, the hardware necessary to do so, and the sensing elements that will be activated to detect damage packaged for rapid, repeated, re-deployment by end users through autonomous localization.

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Image Processing in Road Traffic Analysis

Cited by 36 publications

References 0 publications

Detection of Traffic Density With Image Processing Using Pin Hole Algorithm

Detection of Traffic Density With Image Processing Using Pin Hole Algorithm

An Image Processing Approach to Detect Lanes, Pot Holes and Recognize Road Signs in Indian Roads

A Citizen-Centric Health Monitoring Paradigm Using Embedded Self-Locating Wireless Sensor Networks

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