Real time speed bump detection using Gaussian filtering and connected component approach

Devapriya, W.; Babu, C. Nelson Kennedy; Srihari, T.

doi:10.1109/startup.2016.7583981

Cited by 26 publications

(17 citation statements)

References 8 publications

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“…This section introduces the novel technique for speed breaker, road marking detection and recognition, presenting the functionality of each module. As proposed in [2], a methodology that involves 4 stages detected speed bump. Adopting similar method, on our dataset; it failed due to addition road marking "STOP".…”

Section: Proposed System Methodologymentioning

confidence: 99%

“…Because many speed bumps are constructed without proper permission. Not notifying of speed bump over high speed is harmful for patients in transit, pregnant women, rapid wear and tear and damage to vehicles [2]. They went further to suggest the need to develop a system that services the end user driver using image processing algorithms such as Gaussian filtering, Median filtering and Connected Component Approach.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The system is developed to alert a driver or to interact directly on the vehicle for safety and better driving. DAS includes Driver drowsiness detection, Adaptive Cruise Control (ACC), Lane departure warning system, Traffic sign recognition, Wrong-way driving warning, automotive navigation system, and so on [2]. The purpose of Advanced Driver Assistance Systems (ADAS) is that driver error will be reduced or even eliminated, and efficiency in traffic and transport is enhanced.…”

Section: Introductionmentioning

confidence: 99%

“…According to Rahayu et al [6] speed breaker detection is important to community in order to save the car and human life. Authors in [2] suggested the need to develop a system that services the end user driver using image processing algorithms such as Gaussian filtering, Median filtering and Connected Component Approach etc. The state-of-art algorithm suggested by Devapriya et al in [2] fails on datasets that includes stop markings.…”

Section: Introductionmentioning

confidence: 99%

“…Authors in [2] suggested the need to develop a system that services the end user driver using image processing algorithms such as Gaussian filtering, Median filtering and Connected Component Approach etc. The state-of-art algorithm suggested by Devapriya et al in [2] fails on datasets that includes stop markings. Hence there is a need for an algorithm that provides a solution to the state-of-art algorithm problem.…”

Section: Introductionmentioning

confidence: 99%

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Speed Breakers, Road Marking Detection and Recognition Using Image Processing Techniques

Adeyemi

Irhebhude

Kolawole

2019

AIVP

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This paper presents a image processing technique for speed breaker, road marking detection and recognition. An Optical Character Recognition (OCR) algorithm was used to recognize traffic signs such as “STOP” markings and a Hough transform was used to detect line markings which serves as a pre-processing stage to determine when the proposed technique does OCR or speed breaker recognition. The stopline inclusion serves as a pre-processing stage that tells the system when to perform stop marking recognition or speed breaker recognition. Image processing techniques was used for the processing of features from the images. Local Binary Pattern (LBP) was extracted as features and employed to train the Support Vector Machine (SVM) classifier for speed breaker recognition. Experimental results shows 79%, 100% “STOP” sign and speed breaker recognitions respectively. The proposed system goes very well for the roads which are constructed with proper painting irrespective of their dimension.

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Section: Proposed System Methodologymentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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