A Novel Approach for Night-Time Vehicle Detection in Real-Time Scenario

Aswin, Muhammad; Brindha, G.

doi:10.1007/978-981-10-8575-8_12

Cited by 2 publications

(3 citation statements)

References 3 publications

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“…61, No. 7, pp: 1811-1822 1817 Aswin et al [17] proposed a vehicle detection system for counting and classification of the vehicles during the night. The system involves preprocessing operations such the background subtraction and image segmentation.…”

Section: Najm and Alimentioning

confidence: 99%

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Automatic Vehicles Detection, Classification and Counting Techniques / Survey

Najm

Ali

2020

eijs

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Vehicle detection (VD) plays a very essential role in Intelligent Transportation Systems (ITS) that have been intensively studied within the past years. The need for intelligent facilities expanded because the total number of vehicles is increasing rapidly in urban zones. Trafﬁc monitoring is an important element in the intelligent transportation system, which involves the detection, classification, tracking, and counting of vehicles. One of the key advantages of traffic video detection is that it provides traffic supervisors with the means to decrease congestion and improve highway planning. Vehicle detection in videos combines image processing in real-time with computerized pattern recognition in flexible stages. The real-time processing is very critical to keep the appropriate functionality of automated or continuously working systems. VD in road traffics has numerous applications in the transportation engineering field. In this review, different automated VD systems have been surveyed, with a focus on systems where the rectilinear stationary camera is positioned above intersections in the road rather than being mounted on the vehicle. Generally, three steps are utilized to acquire traffic condition information, including background subtraction (BS), vehicle detection and vehicle counting. First, we illustrate the concept of vehicle detection and discuss background subtraction for acquiring only moving objects. Then a variety of algorithms and techniques developed to detect vehicles are discussed beside illustrating their advantages and limitations. Finally, some limitations shared between the systems are demonstrated, such as the definition of ROI, focusing on only one aspect of detection, and the variation of accuracy with quality of videos. At the point when one can detect and classify vehicles, then it is probable to more improve the flow of the traffic and even give enormous information that can be valuable for many applications in the future.

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Section: Najm and Alimentioning

confidence: 99%

“…The authors [17] proposed system is VD in night time system which involves preprocessing operations such as background subtraction, image segmentation, and blob analysis.…”

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confidence: 99%

Automatic Vehicles Detection, Classification and Counting Techniques / Survey

Najm

Ali

2020

eijs

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“…Front vehicles are the road obstacles in the actual driving environment, and vehicle detection refers to automatically detecting the vehicle in front from the collected pictures or video streams and making proper positioning. In the actual road scenes, bad weather conditions, complicated traffic environments, varying degrees of object occlusion, and differences in the vehicle characteristics make vehicle detection a challenging task [ 10 , 11 , 12 ]. Experts and scholars worldwide have shown increasing interest in the research of vehicle detection, with the development and improvement of intelligent transportation system and computer vision technology.…”

Section: Introductionmentioning

confidence: 99%

Front Vehicle Detection Algorithm for Smart Car Based on Improved SSD Model

Cao

Song

et al. 2020

Sensors

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Vehicle detection is an indispensable part of environmental perception technology for smart cars. Aiming at the issues that conventional vehicle detection can be easily restricted by environmental conditions and cannot have accuracy and real-time performance, this article proposes a front vehicle detection algorithm for smart car based on improved SSD model. Single shot multibox detector (SSD) is one of the current mainstream object detection frameworks based on deep learning. This work first briefly introduces the SSD network model and analyzes and summarizes its problems and shortcomings in vehicle detection. Then, targeted improvements are performed to the SSD network model, including major advancements to the basic structure of the SSD model, the use of weighted mask in network training, and enhancement to the loss function. Finally, vehicle detection experiments are carried out on the basis of the KITTI vision benchmark suite and self-made vehicle dataset to observe the algorithm performance in different complicated environments and weather conditions. The test results based on the KITTI dataset show that the mAP value reaches 92.18%, and the average processing time per frame is 15 ms. Compared with the existing deep learning-based detection methods, the proposed algorithm can obtain accuracy and real-time performance simultaneously. Meanwhile, the algorithm has excellent robustness and environmental adaptability for complicated traffic environments and anti-jamming capabilities for bad weather conditions. These factors are of great significance to ensure the accurate and efficient operation of smart cars in real traffic scenarios and are beneficial to vastly reduce the incidence of traffic accidents and fully protect people’s lives and property.

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A Novel Approach for Night-Time Vehicle Detection in Real-Time Scenario

Cited by 2 publications

References 3 publications

Automatic Vehicles Detection, Classification and Counting Techniques / Survey

Automatic Vehicles Detection, Classification and Counting Techniques / Survey

Front Vehicle Detection Algorithm for Smart Car Based on Improved SSD Model

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