Intelligent all-day vehicle detection based on decision-level fusion using color and thermal sensors

Chien, Shih‐Chieh; Chang, Feng-Chia; Tsai, Chiung-Cheng; Chen, Yung-Yao

doi:10.1109/aris.2017.8297189

Cited by 10 publications

(5 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The light is brighter, or more white and lighter, as the size of the data increases. The computer can determine the color of the body because we have classified body colors in the database [18].…”

Section: Recognition Of Body Colormentioning

confidence: 99%

A Traffic Parameter Detection Algorithm Based on Double Coils

Wu¹,

Sun²,

Cao³

2022

View full text Add to dashboard Cite

This paper designs a detection method for real-time acquisition of traffic intersection parameters based on image processing. Firstly, two virtual coils were drawn at the beginning and the end on the acquired video stream image. Next, the presence of the vehicle was judged based on the change of the gray value in the two virtual coils. The background difference algorithm was adopted to detect and obtain various traffic parameters. The experimental video samples were collected in the actual road environment. Our algorithm relies on the first and last two coils for information processing, and completes vehicle detection, speed detection, and important parameter information acquisition of body color. Experiments show that, compared with the traditional machine vision-based traffic information detection algorithm, our traffic parameter detection method, which is based on double virtual coils, has high detection accuracy, and can detect more parameter information, while satisfying real-time performance.

show abstract

Section: Recognition Of Body Colormentioning

confidence: 99%

A Traffic Parameter Detection Algorithm Based on Double Coils

Wu¹,

Sun²,

Cao³

2022

View full text Add to dashboard Cite

show abstract

“…For certain cases, because of the fundamental differences between visual and infrared imaging, the techniques used to detect pedestrians in the visible spectrum cannot be extended to infrared images, and other approaches must be used. In [ 172 , 173 ], fusion between the thermal camera and regular visible camera for the detection task is presented and the same comparison of detection techniques can be noted. In [ 174 , 175 ], thorough research on the detection of pedestrians using an FIR camera was presented.…”

Section: Sensorsmentioning

confidence: 99%

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

Mohammed

Amamou

Ayevide

et al. 2020

Sensors

View full text Add to dashboard Cite

Perception is a vital part of driving. Every year, the loss in visibility due to snow, fog, and rain causes serious accidents worldwide. Therefore, it is important to be aware of the impact of weather conditions on perception performance while driving on highways and urban traffic in all weather conditions. The goal of this paper is to provide a survey of sensing technologies used to detect the surrounding environment and obstacles during driving maneuvers in different weather conditions. Firstly, some important historical milestones are presented. Secondly, the state-of-the-art automated driving applications (adaptive cruise control, pedestrian collision avoidance, etc.) are introduced with a focus on all-weather activity. Thirdly, the most involved sensor technologies (radar, lidar, ultrasonic, camera, and far-infrared) employed by automated driving applications are studied. Furthermore, the difference between the current and expected states of performance is determined by the use of spider charts. As a result, a fusion perspective is proposed that can fill gaps and increase the robustness of the perception system.

show abstract

“…Two halos of the headlights are intersected when the distance between the vehicle and the camera is greater than D tangent . The vehicle headlight halo area S RealLight is expressed by Equation (15).…”

Section: Low Probability Candidate Filtermentioning

confidence: 99%

“…The unsupervised learning was applied for the classification of vehicles successfully [12,13]. Furthermore, Convolutional Neural Networks (CNNs), YOLO [14], and other neural networks have made outstanding contributions to vehicle detection in both RGB images and thermal images [11,15,16]. However, more relevant optimization and adjustment are needed to obtain a more suitable training model.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Adjustment and Distinguishing Method for Vehicle Headlight Based on Data Access of a Thermal Camera

Bai

Qin

2020

Front. Phys.

View full text Add to dashboard Cite

In recent years, more and more traffic accidents have been caused by the illegal use of high beams. Therefore, the distinguishing of the vehicle headlight is vital for night driving and traffic supervision. Then, a method for distinguishing vehicle headlight based on data access of a thermal camera was proposed in this paper. There are two steps in this method. The first step is thermal image dynamic adjustment. In thermal image dynamic adjustment, the details of thermal images were enhanced by adjusting the temperature display dynamically and fusing features of multi-sequence images. The second step is vehicle headlight dynamic distinguishing, and features of vehicle headlight were extracted by YOLOv3. Then, the high beam and low beam were further distinguished by the filter based on the position and proportion relationship between the halo and the beam size of vehicle headlights. In addition, the accessed thermal image dataset during the night was used for training purposes. The results showed that the precision of this method was 94.2%, and the recall was 78.7% at a real-time speed of 9 frames per second (FPS). Compared with YOLOv3 on the Red Green Blue (RGB) image, the precision was further improved by 11.1%, and the recall was further improved by 5.1%. Dynamic adjustment and distinguishing method were also applied in single-shot multibox detector (SSD) network which has good performance in small-object detection. Compared with the SSD network on the RGB image, the precision was improved by 8.2% and the recall was improved by 4.6% when SSD network was improved by this method.

show abstract

Intelligent all-day vehicle detection based on decision-level fusion using color and thermal sensors

Cited by 10 publications

References 2 publications

A Traffic Parameter Detection Algorithm Based on Double Coils

A Traffic Parameter Detection Algorithm Based on Double Coils

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

Dynamic Adjustment and Distinguishing Method for Vehicle Headlight Based on Data Access of a Thermal Camera

Contact Info

Product

Resources

About