Image processing based vehicle detection and tracking method

Bhaskar, Prem Kumar; Yong, Suet-Peng

doi:10.1109/iccoins.2014.6868357

Cited by 75 publications

(25 citation statements)

References 9 publications

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“…Bhaskar et al [2] propose an image processing based vehicle detection and tracking method. The traffic data will automatically extract from this method.…”

Section: Related Workmentioning

confidence: 99%

An Obstacle Detection Scheme for Vehicles in an Intelligent Transportation System

Shah¹,

Maru²,

Jhaveri³

2016

IJCNIS

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Abstract-Road obstacles cause serious accidents that have a severe impact on driver safety, traffic flow efficiency and damage of the vehicle. Detecting obstacles are important to prevent or to reduce such kind of the accidents and fatalities. However, it is difficult and becomes tricky because of some problems like presence of shadow, environmental changes or a sudden action of any moving things (e.g., car overtaking, animal coming) and many more. Thereby, this paper aims to design an obstacle detection technique based on (i) moving cameras and (ii) moving objects. These methods are applied to obstacle detection phase, in order to identify the different obstacles (e.g., potholes, animals, stop sign, obstacles, bumps, road cracks) by considering road dimensions. A new technique is introduced for detecting obstacles from moving camera and moving objects which overcomes several limitations over stationary cameras and moving/stationary objects. Further, paper reviews recent research trends to detect obstacles for moving cameras and moving objects with discussion of key points and limitations of each approach. Finally, the results show that the proposed method is more robust and reliable than the previous approaches based on the stationary cameras.

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“…Bhaskar et al [2] propose an image processing based vehicle detection and tracking method. The traffic data will automatically extract from this method.…”

Section: Related Workmentioning

confidence: 99%

An Obstacle Detection Scheme for Vehicles in an Intelligent Transportation System

Shah¹,

Maru²,

Jhaveri³

2016

IJCNIS

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“…The system detects and tracks vehicles while they pass through the camera, providing information such as vehicle count, average speed, or traffic congestions [5][6][7]. To achieve it, an algorithm must be created that segments and tracks the vehicle movements within the video [6,8,9].…”

Section: Introductionmentioning

confidence: 99%

An Alternative Vehicle Counting Tool Using the Kalman Filter within MATLAB

et al. 2017

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This study proposes an alternative and economical tool to estimate traffic densities, via video-image processing adapting the Kalman filter included in the Matlab code. Traffic information involves acquiring data for long periods of time at stationary points. Vehicle counting is vital in modern transport studies, and can be achieved by using different techniques, such as manual counts, use of pneumatic tubes, magnetic sensors, etc. In this research however, automatic vehicle detection was achieved using image processing, because it is an economical and sometimes even faster option. Commercial automatic vehicle detection and tracking programs/applications already exist, but their use is typically prohibitive due to their high cost. Large cities can obtain traffic recordings from surveillance cameras and process the information, but it is difficult for smaller towns without such infrastructure or even assigned budget. The proposed tool was developed taking into consideration these difficult situations, and it only requires users to have access to a fixed video camera placed at an elevated point (e.g. a pedestrian bridge or a light pole) and a computer with a powerful processor; the images are processed automatically through the Kalman filter code within Matlab. The Kalman filter predicts random signals, separates signals from random noise or detects signals with the presence of noise, minimizing the estimated error. It needs nevertheless some adjustments to focus it for vehicle counting. The proposed algorithm can thus be adapted to fit the users' necessities and even the camera's position. The use of this algorithm allows to obtain traffic data and may help small cities´ decision makers dealing with present and future urban planning and the design or installment of transportation systems.

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“…As shown in this figure, our algorithm extracts the background from the input images. The input images have in them some objects that move, so similar to [16] an iterative (frame-by-frame) algorithm is proposed to extract the background and update it in the successive frames.…”

Section: ) Object Detectionmentioning

confidence: 99%

“…In Bhaskar et al [16], a unique algorithm for vehicle data recognition and tracking using Gaussian mixture model and blob detection methods is proposed.…”

Section: Introductionmentioning

confidence: 99%

Real-time search-free multiple license plate recognition via likelihood estimation of saliency

Safaei

Tang

Sanei

2016

Computers & Electrical Engineering

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In this paper, we propose a novel search-free localization method based on 3-D Bayesian saliency estimation. This method uses a new 3-D object tracking algorithm which includes: object detection, shadow detection and removal, and object recognition based on Bayesian methods. The algorithm is tested over three image datasets with different levels of complexities, and the results are compared with those of benchmark methods in terms of speed and accuracy. Unlike most search-based license-plate extraction methods, our proposed 3-D Bayesian saliency algorithm has lower execution time (less than 60 ms), more accuracy , and it is a search-free algorithm which works in noisy backgrounds.

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Image processing based vehicle detection and tracking method

Cited by 75 publications

References 9 publications

An Obstacle Detection Scheme for Vehicles in an Intelligent Transportation System

An Obstacle Detection Scheme for Vehicles in an Intelligent Transportation System

An Alternative Vehicle Counting Tool Using the Kalman Filter within MATLAB

Real-time search-free multiple license plate recognition via likelihood estimation of saliency

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