Traffic light detection and recognition based on Haar-like features

Lee, Sanghyuk; Kim, Jung-Hawn; Lim, Yong-Jin; Lim, Jinho

doi:10.23919/elinfocom.2018.8330598

Cited by 15 publications

(6 citation statements)

References 9 publications

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“…In the processing of the video recording two general steps are distinguished, the identification of frames, that is images of the video, and their later processing. In this latter step assorted techniques could be applied but, given the need of identifying light points, the use of machine learning algorithms or the management of grayscale images, are common approaches already used, e.g., for traffic light detection [31], [32]. Therefore, SmartLED follows four steps in this regard.…”

Section: F Video Processingmentioning

confidence: 99%

SmartLED: Smartphone-Based Covert Channels Leveraging the Notification LED

González-Manzano

Bernardez

Fuentes

2020

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

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The widespread adoption of smartphones make them essential in daily routines. Thus, they can be used to create a covert channel without raising suspicions. To avoid detection, networkless communications are preferred. In this paper, we propose SmartLED, a mechanism to build covert channels leveraging a widely available smartphone feature -its notification LED. The secret is encoded through LED blinks using Manhattan encoding. SmartLED is assessed in real-world indoor and outdoor scenarios, considering different distances up to 5 meters. Our results show that the best performance is achieved in dark settings -34.8 s. are needed to exfiltrate a 7-byte password to a distance of 1 m. Remarkably, distance does not cause a great impact on effective transmission time and shorter blinks do not lead to substantially greater transmission errors.

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Section: F Video Processingmentioning

confidence: 99%

SmartLED: Smartphone-Based Covert Channels Leveraging the Notification LED

González-Manzano

Bernardez

Fuentes

2020

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

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“…The contribution by [8], presents a system composed of the pre-learned Haar-like feature and SVM classifier. The researcher uses Haar-like features to learn about the traffic light image and detect the candidate area based on the learning data.…”

Section: Detection Of Traffic Light Using Machine Vision For Autonomomentioning

confidence: 99%

Detection of Traffic Light using Machine Vision for Autonomous Vehicles Application

2019

IJEAT

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Traffic light detection is crucial to decrease the traffic light accidents at intersections and to realize autonomous driving. There are so many existing methods to detect traffic light. However, these approaches have several limitations, such as not function well in complex driving environments. Hence, to overcome such constraints, the traffic light detection for the autonomous vehicle using image processing technique is proposed. The experiments are carried out using 114 scene images that consist of 209 traffic lights with different angles, weather conditions, and distance. An image processing technique, Hough Circle Transform is used in this traffic light detection system with the help of Gaussian blurring and Sobel filter. So, the overall accuracy rate for the proposed algorithm is 75.59%. This system is possible to be used in urban areas or complex environments, whether it is at night or day, and it able to detect the traffic light regardless of the colour changes.

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“…Besides, the main goal of the object recognition stage is to achieve accurate object classification through manual feature extraction and machine learning classification. The typical manual features have good immutability, such as SIFT, 12 HOG, 13,14 Haar-like, 13,14 but the recognition accuracy is significantly reduced when the quality of the image is worse. Simultaneously, the commonly used machine learning algorithms such as AdaBoost, 9 SVM 10 run fast, while the recognition accuracy is difficult to satisfy the practical requirements of autonomous driving.…”

Section: Introductionmentioning

confidence: 99%

An improved traffic lights recognition algorithm for autonomous driving in complex scenarios

Zeng

Liu³

et al. 2021

International Journal of Distributed Sensor Networks

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Image recognition is susceptible to interference from the external environment. It is challenging to accurately and reliably recognize traffic lights in all-time and all-weather conditions. This article proposed an improved vision-based traffic lights recognition algorithm for autonomous driving, integrating deep learning and multi-sensor data fusion assist (MSDA). We introduce a method to obtain the best size of the region of interest (ROI) dynamically, including four aspects. First, based on multi-sensor data (RTK BDS/GPS, IMU, camera, and LiDAR) acquired in a normal environment, we generated a prior map that contained sufficient traffic lights information. And then, by analyzing the relationship between the error of the sensors and the optimal size of ROI, the adaptively dynamic adjustment (ADA) model was built. Furthermore, according to the multi-sensor data fusion positioning and ADA model, the optimal ROI can be obtained to predict the location of traffic lights. Finally, YOLOv4 is employed to extract and identify the image features. We evaluated our algorithm using a public data set and actual city road test at night. The experimental results demonstrate that the proposed algorithm has a relatively high accuracy rate in complex scenarios and can promote the engineering application of autonomous driving technology.

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Traffic light detection and recognition based on Haar-like features

Cited by 15 publications

References 9 publications

SmartLED: Smartphone-Based Covert Channels Leveraging the Notification LED

SmartLED: Smartphone-Based Covert Channels Leveraging the Notification LED

Detection of Traffic Light using Machine Vision for Autonomous Vehicles Application

An improved traffic lights recognition algorithm for autonomous driving in complex scenarios

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