RETRACTED ARTICLE: An intelligent multiple vehicle detection and tracking using modified vibe algorithm and deep learning algorithm

Sudha, D.; Priyadarshini, J.

doi:10.1007/s00500-020-05042-z

Cited by 54 publications

(29 citation statements)

References 20 publications

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“…These results indicate that the motor control is adjusted to the realistic attributes of the visual scene [5]. Sudha et al [6] proposed an advanced deep learning method called "enhanced," a V3 and improved visual background extractor algorithm for detecting multiple types and multiple vehicles in the input video. More precisely, tracking is done using a combination of the Kalman filter algorithm and particle filter technology to find the trajectory of the incoming vehicle.…”

Section: Introductionmentioning

confidence: 98%

[Retracted] Research on the Detection and Tracking Algorithm of Moving Object in Image Based on Computer Vision Technology

Chen

2021

Wireless Communications and Mobile Computing

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In order to improve the video image processing technology, this paper presents a moving object detection and tracking algorithm based on computer vision technology. Firstly, the detection performance of the interframe difference method and the background difference model method is compared comprehensively from both theoretical and experimental aspects, and then the Robert edge detection operator is selected to carry out edge detection of the vehicle. The research results show that the algorithm proposed in this paper has the longest running time per frame when tracking a moving target, which is about 2.3 times that of the single frame running time of the CamShift algorithm. The algorithm has high running efficiency and can meet the requirements of real-time tracking of a foreground target. The algorithm has the highest tracking accuracy, the time consumption is reduced, and the error of the tracking frame deviating from the real position of the target is the least.

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Section: Introductionmentioning

confidence: 98%

[Retracted] Research on the Detection and Tracking Algorithm of Moving Object in Image Based on Computer Vision Technology

Chen

2021

Wireless Communications and Mobile Computing

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“…Airports, banks, casinos, highways, stadiums, crowd gathering areas, buildings, streets, railway stations, department stores, and all government organizations now use video surveillance to reinforce their security. Video surveillance is employed in almost every aspect of society to deter criminal activity and improve public safety and security [35]. A large typical building in a major city has a vast network of cameras installed on main floors, entrances, huge gathering areas, hallways, offices, and labs.…”

Section: Research Areasmentioning

confidence: 99%

“… Human-computer interaction: Gestures [35] have long been thought of as an inter-action approach that may help us communicate with our computers in more creative, natural, and intuitive ways [39]. Many modern apps rely on human-computer interaction are developed.…”

Section: Research Areasmentioning

confidence: 99%

A WSM-based Comparative Study of Vision Tracking Methodologies

Bouraya¹,

Belangour²

2021

IJACSA

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Vision tracking is a key component of a video sequence. It is the process of locating single or multiple moving objects over time using one or many cameras. The latter's function consists of detecting, categorizing, and tracking. The development of the trustworthy solution for video sequence analysis opens up new horizons for a variety of applications, including intelligent transportation systems, biomedical, agriculture, human-machine interaction, augmented reality, video surveillance, robots, and many crucial research areas. To make efficient models, there are challenges in video observation to deal with, such as problems with the environment, light variation, pose variation, motion blur, clutter, occlusion, and so on. In this paper, we present several techniques that addressed the issues of detecting and tracking multiple targets on video sequences. The proposed comparative study relied on different methodologies. This paper's purpose is to list various approaches, classify them, and compare them, using the Weighted Scoring Model (WSM) comparison method. This includes studying these algorithms, selecting relevant comparison criteria, assigning weights for each criterion, and lastly computing scores. The obtained results of this study will reveal the strong and weak points of each algorithm mentioned and discussed.

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“…In general, object recognition artificial intelligence algorithms including the YOLOv3 algorithm shows the time complexity of O(n) owing to the iterative computations of the image arrays [39,40]. The vehicle parts recognition module proposed in this study had a time complexity of O(log n) because it does not analyze all images depending on the situation by using the MSSIM image processing technique.…”

Section: Proposal and Development For Improving Performance Of Vehiclmentioning

confidence: 99%

Design and Implementation of a Video/Voice Process System for Recognizing Vehicle Parts Based on Artificial Intelligence

Kim

Jeong

2020

Sensors

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With the recent development of artificial intelligence along with information and communications infrastructure, a new paradigm of online services is being developed. Whereas in the past a service system could only exchange information of the service provider at the request of the user, information can now be provided by automatically analyzing a particular need, even without a direct user request. This also holds for online platforms of used-vehicle sales. In the past, consumers needed to inconveniently determine and classify the quality of information through static data provided by service and information providers. As a result, this service field has been harmful to consumers owing to such problems as false sales, fraud, and exaggerated advertising. Despite significant efforts of platform providers, there are limited human resources for censoring the vast amounts of data uploaded by sellers. Therefore, in this study, an algorithm called YOLOv3+MSSIM Type 2 for automatically censoring the data of used-vehicle sales on an online platform was developed. To this end, an artificial intelligence system that can automatically analyze an object in a vehicle video uploaded by a seller, and an artificial intelligence system that can filter the vehicle-specific terms and profanity from the seller’s video presentation, were also developed. As a result of evaluating the developed system, the average execution speed of the proposed YOLOv3+MSSIM Type 2 algorithm was 78.6 ms faster than that of the pure YOLOv3 algorithm, and the average frame rate per second was improved by 40.22 fps. In addition, the average GPU utilization rate was improved by 23.05%, proving the efficiency.

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RETRACTED ARTICLE: An intelligent multiple vehicle detection and tracking using modified vibe algorithm and deep learning algorithm

Cited by 54 publications

References 20 publications

[Retracted] Research on the Detection and Tracking Algorithm of Moving Object in Image Based on Computer Vision Technology

[Retracted] Research on the Detection and Tracking Algorithm of Moving Object in Image Based on Computer Vision Technology

A WSM-based Comparative Study of Vision Tracking Methodologies

Design and Implementation of a Video/Voice Process System for Recognizing Vehicle Parts Based on Artificial Intelligence

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