Vehicle Recognition Method Based on Color Invariant SIFT Features

Xu, Yaming; Zhang, Juan; Liu, Chengcheng; Gu, Juping; Hua, Liu

doi:10.23919/chicc.2018.8484020

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…Due to the slow computing speed of the traditional SIFT method, in [ 162 ] vehicle feature was extracted by the Dense-SIFT method, so as to realize the detection of remote moving vehicles and improve the computing efficiency. In [ 163 ], the color invariant “CI-SIFT” was designed to enable it to have good characteristics when detecting vehicles of different colors. The author first recognized the body color through HSV color space, and then extracted the features through CI-SIFT, finally, vehicle detection was realized based on the matching algorithm.…”

Section: Vehicle Detection: Vision-based Methodsmentioning

confidence: 99%

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Liu

Yuan

et al. 2021

Sensors

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Unmanned ground vehicles (UGVs) have great potential in the application of both civilian and military fields, and have become the focus of research in many countries. Environmental perception technology is the foundation of UGVs, which is of great significance to achieve a safer and more efficient performance. This article firstly introduces commonly used sensors for vehicle detection, lists their application scenarios and compares the strengths and weakness of different sensors. Secondly, related works about one of the most important aspects of environmental perception technology—vehicle detection—are reviewed and compared in detail in terms of different sensors. Thirdly, several simulation platforms related to UGVs are presented for facilitating simulation testing of vehicle detection algorithms. In addition, some datasets about UGVs are summarized to achieve the verification of vehicle detection algorithms in practical application. Finally, promising research topics in the future study of vehicle detection technology for UGVs are discussed in detail.

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Section: Vehicle Detection: Vision-based Methodsmentioning

confidence: 99%

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Liu

Yuan

et al. 2021

Sensors

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show abstract

“…In [2] aiming at the problems of the color information lack for SIFT features and the interference of color on vehicle identification, a vehicle identification method based on color invariant SIFT features is proposed. Firstly, the color edge regions of the images are calculated by using the RGB information, and the SIFT color invariant feature description vectors are generated in conjunction with the grey information.…”

Section: Literature Reviewmentioning

confidence: 99%

Application access for chromatic color detection

Umarani,

Dimpusowjanya,

Poojitha

et al. 2023

J. Phys.: Conf. Ser.

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The process of identifying specific examples of an item inside an image or video is referred to as “object detection,” and it is one of the computer vision techniques. When individuals look at pictures or movies, they are able to recognize and find things that are of interest to them. The primary goal of the field of object recognition is to achieve this level of intelligence via the use of a computer. Image segmentation and blob analysis, which uses simple object properties such as size, shape, or color, and Feature-based object detection, which uses feature extraction, matching, and RANSAC to estimate the location of an object, are two of the many common techniques that are used in conjunction with deep learning and machine learning-based object detection. There are also many other common techniques that are used in conjunction with deep learning and machine learning-based object detection. The most challenging aspects of object detection are the processes of categorizing things and pinpointing their locations. When seen from a different perspective, the same object might seem to be completely different. Detection algorithms have a tough time distinguishing between various things at different scales and perspectives since the sizes and proportions of items almost always alter. The camera input would be used to determine an object’s shape, according to this project’s proposal. Circle, square, and triangle are the three distinct shapes that we are determining an item to be right now. The finished result is shown in MATLAB’s graphical user interface (GUI). The productive identification process is carried out with the assistance of MATLAB. Using the SFTA Algorithm, the application extracts features from the specific image of a video. The neural network algorithm is used to classify the image of video. Image segmentation and image detection are two examples of related computer vision methods that are inextricably intertwined with object recognition. These techniques allow us to better comprehend and analyze the situations shown in movies and still photographs. It has been determined whether or not the suggested system is suitable. Because of this, the testing has shown that the suggested system generates generally accurate indications of real performance at a rate of 99.4%.

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Vehicle Recognition Method Based on Color Invariant SIFT Features

Cited by 2 publications

References 3 publications

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Application access for chromatic color detection

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