Vehicle Tracking Using Kalman Filter and Features

Salarpour, Amir; Salarpour, Arezoo; Fathi, Mahmoud; Dezfoulian, Mir Hossein

doi:10.5121/sipij.2011.2201

Cited by 24 publications

(14 citation statements)

References 13 publications

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“…P post = A i P prior A T i + Q r ; P post = P prior + Q r (13) where P post is the covariance of the predicted state. The correction steps of the Kalman filter can be expressed through the following Equations (14), (15) and (17).…”

Section: Curve Lane Detection Based On Kalman Filter and Parabola Equmentioning

confidence: 99%

“…In our paper, we introduce a curve lane detection algorithm based on Kalman filter [17]. This algorithm includes Otsu's threshold method [18,19] to convert RGB to Black-White image, image pre-processing using top view image transform [20,21] to create a top-view image of the road, a Hough transform for detecting the straight lane in the near-field of the sensor [22], and parameter estimation of the curve lane using a Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

“…This research is considered on a curve lane detection algorithm, which can estimate parameters of the road turning and define geometric shapes based on the mathematical model and the Kalman filter [17]. This research is considered on a curve lane detection algorithm, which can estimate parameters of the road turning and define geometric shapes based on the mathematical model and the Kalman filter [17].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Highly Curved Lane Detection Algorithms Based on Kalman Filter

2020

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The purpose of the self-driving car is to minimize the number casualties of traffic accidents. One of the effects of traffic accidents is an improper speed of a car, especially at the road turn. If we can make the anticipation of the road turn, it is possible to avoid traffic accidents. This paper presents a cutting edge curve lane detection algorithm based on the Kalman filter for the self-driving car. It uses parabola equation and circle equation models inside the Kalman filter to estimate parameters of a using curve lane. The proposed algorithm was tested with a self-driving vehicle. Experiment results show that the curve lane detection algorithm has a high success rate. The paper also presents simulation results of the autonomous vehicle with the feature to control steering and speed using the results of the full curve lane detection algorithm.

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Section: Curve Lane Detection Based On Kalman Filter and Parabola Equmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Highly Curved Lane Detection Algorithms Based on Kalman Filter

2020

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“…Another paper [3] illustrates the detection and tracking of humans from a single video sequence using a pixel based motion detector to find region of interest (ROI) while using a model based approach. Paper [4] tracks a locomotive object by the Kalman Filter and its features. It detects all mobile objects in the frame.…”

Section: Relevant Research Workmentioning

confidence: 99%

Object Recognition, Tracking and Trajectory Generation in Real-Time Video Sequence

Fatima¹

2013

IJIEE

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The aim of tracking is to detect objects through images. This paper focuses on detection of moving objects in a scene and tracking of the objects as long as they remain in camera view. Initially noise removal and image enhancement is carried out. The objects are detected from the supervised Euclidean segmentation of the image frames. An object recognition algorithm then classifies the segmented objects by employing minimum distance classifiers approach, and by comparing various length and shape descriptors. Finally, object trajectory is generated based on centroid location of geometric object. For the case study, we have considered tracking of a tennis ball and shown its trajectory after tracking. This method assures accurate image segmentation via specifying color intensities of the object. Although median based approach gives much better results but it's rather much more expensive. Same case is with Census Transform method that is computationally expensive and is complex if multiple objects are there per frame.

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“…Video tracking of moving multiple objects is a topic of interest in the research community. This function was applied for vehicle tracking using Kalman filter [1]. Concerning this function three main approaches to detect and segment the vehicles such as background subtraction method, features based method, and frame differencing and motion based method were studied [2].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of a Video Surveillance System Based on Motion Detection

Massaro¹,

Vitti²,

Maurantonio³

et al. 2018

SIPIJ

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The implementation of a stand-alone system developed in JAVA language for motion detection has been discussed. The open-source OpenCV library has been adopted for video surveillance image processing thus implementing Background Subtraction algorithm also known as foreground detection algorithm. Generally the region of interest of a body or object to detect is related to a precise objects (people, cars, etc.) emphasized on a background. This technique is widely used for tracking a moving objects. In particular, the BackgroundSubtractorMOG2 algorithm of OpenCV has been applied. This algorithm is based on Gaussian distributions and offers better adaptability to different scenes due to changes in lighting and the detection of shadows as well. The implemented webcam system relies on saving frames and creating GIF and JPGs files with previously saved frames. In particular the Background Subtraction function, find Contours, has been adopted to detect the contours. The numerical quantity of these contours has been compared with the tracking points of sensitivity obtained by setting an user-modifiable slider able to save the frames as GIFs composed by different merged JPEGs. After a full design of the image processing prototype different motion test have been performed. The results showed the importance to consider few sensitivity points in order to obtain more frequent image storages also concerning minor movements. Sensitivity points can be modified through a slider function and are inversely proportional to the number of saved images. For small object in motion will be detected a low percentage of sensitivity points. Experimental results proves that the setting condition are mainly function of the typology of moving object rather than the light conditions. The proposed prototype system is suitable for video surveillance smart camera in industrial systems.

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Vehicle Tracking Using Kalman Filter and Features

Abstract: ABSTRACT

Cited by 24 publications

References 13 publications

Highly Curved Lane Detection Algorithms Based on Kalman Filter

Highly Curved Lane Detection Algorithms Based on Kalman Filter

Object Recognition, Tracking and Trajectory Generation in Real-Time Video Sequence

Sensitivity of a Video Surveillance System Based on Motion Detection

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