Optical Flow Algorithm for Velocity Estimation of Ground Vehicles: A Feasibility Study

Chhaniyara, S.; Bunnun, Pished; Seneviratne, Lakmal; Althoefer, Kaspar

doi:10.21307/ijssis-2017-289

Cited by 21 publications

(11 citation statements)

References 24 publications

(24 reference statements)

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“…The optical flow block reads image intensity value and estimate the velocity of object motion using either the Horn-Schunck or the Lucas-Kanade. [4] The velocity estimation can be either between two images or between current frame and Nth frame back. We set N to be one in our model.…”

Section: Design and Implementationmentioning

confidence: 99%

Comparison of Optical Flow Algorithms for Speed Determination of Moving Objects

Patel¹,

Shukla²

2013

IJCA

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In this paper, we present a semi real-time vehicle tracking algorithm to determine the speed of the vehicles in traffic from traffic cam video. The results of this work can be used for traffic control, security and safety both by government agencies and commercial organizations. In this paper a method is described for tracking moving objects from a sequence of video frame. This method is implemented by using optical flow (Horn-Schunck)and (Lucas-Kanade) in mat lab and Simulink. It has a variety of uses, some of which are: human computer interaction, security and surveillance, video communication and compression, augmented reality, traffic control, medical imaging and video editing. Segmentation is performed to detect the object after reducing the noise from that scene. The object is tracked by plotting a rectangular bounding box around it in each frame. The velocity of the object is determined by calculating the distance that the object moved in a sequence of frames with respect to the frame rate that the video is recorded. Comparison and performance analysis of algorithms based on psnr and average angular error is done.

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Section: Design and Implementationmentioning

confidence: 99%

Comparison of Optical Flow Algorithms for Speed Determination of Moving Objects

Patel¹,

Shukla²

2013

IJCA

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“…[12] A camera was mounted on a height and the video was captured. The calibration parameters were calculated using Calibration toolbox.…”

Section: Resultsmentioning

confidence: 99%

“…[12] While earlier we worked with objectintrinsic properties such as the centroid of a moving object in order to make a probable prediction of its immediate future motion, methods to detect a rectangular boundary for the object, then used background subtraction Simulink models and but didn"t get fair output for multiple vehicles. Further we made an attempt using the Lucas-Kanade method.…”

Section: Discussionmentioning

confidence: 99%

Speed Determination of Moving Vehicles using Lucas-Kanade Algorithm

Shukla¹,

Patel²

2012

IJCATR

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This paper presents a novel velocity estimation method for ground vehicles. The task here is to automatically estimate vehicle speed from video sequences acquired with a fixed mounted camera. The vehicle motion is detected and tracked along the frames using Lucas-Kanade algorithm. The distance traveled by the vehicle is calculated using the movement of the centroid over the frames and the speed of the vehicle is estimated. The average speed of cars is determined from various frames. The application is developed using MATLAB and SIMULINK.

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“…They used Matlab and the Lukas and Kanade algorithm to compute optical flow. They obtained good results at low speeds (0-50 mm/s), however the suitability of the algorithm they used is questionable (Chhaniyara et al 2008). This technology has already found its way to the transportation industry as well.…”

Section: Related Workmentioning

confidence: 99%