Edge-based forward vehicle detection method for complex scenes

Tsai, Wen-Kai; Wu, Shao-Lung; Lin, Li-Juo; Chen, Tse-Min; Li, Minghua

doi:10.1109/icce-tw.2014.6904044

Cited by 7 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Edges are one of the most used features in vehicle detection. Edge-based methods [7,8] build upon the rear view of a vehicle containing many horizontal and vertical structures, e.g., contour of the vehicle, license plate, rear window, bumper, etc. that cause high edge density in the image.…”

Section: Related Workmentioning

confidence: 99%

Shadow-Based Vehicle Detection in Urban Traffic

Ibarra-Arenado

Tjahjadi

Pérez-Oria

et al. 2017

Sensors

View full text Add to dashboard Cite

Vehicle detection is a fundamental task in Forward Collision Avoiding Systems (FACS). Generally, vision-based vehicle detection methods consist of two stages: hypotheses generation and hypotheses verification. In this paper, we focus on the former, presenting a feature-based method for on-road vehicle detection in urban traffic. Hypotheses for vehicle candidates are generated according to the shadow under the vehicles by comparing pixel properties across the vertical intensity gradients caused by shadows on the road, and followed by intensity thresholding and morphological discrimination. Unlike methods that identify the shadow under a vehicle as a road region with intensity smaller than a coarse lower bound of the intensity for road, the thresholding strategy we propose determines a coarse upper bound of the intensity for shadow which reduces false positives rates. The experimental results are promising in terms of detection performance and robustness in day time under different weather conditions and cluttered scenarios to enable validation for the first stage of a complete FACS.

show abstract

Section: Related Workmentioning

confidence: 99%

Shadow-Based Vehicle Detection in Urban Traffic

Ibarra-Arenado

Tjahjadi

Pérez-Oria

et al. 2017

Sensors

View full text Add to dashboard Cite

show abstract

“…Further, the threshold adapts itself dynamically to handle varying light conditions. Wen-Kai Tsai et al [8] proposed an algorithm to extract the horizontal edge as a composition of vehicles by using Sobel filters and orientation gradient calculations. Firstly, to reduce the memory consumption and computation time, regions of interest (ROI) are defined based on the scene and camera type.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Application of Edge Detection for Vehicle Detection in Traffic Surveillance System

Ashwini¹,

Yuvaraju²

2015

IJCA

View full text Add to dashboard Cite

Detecting the vehicles and having a detailed behavior analysis of the vehicles and their behavior in a traffic surveillance system is an emerging area of research. Vehicle detection would be the first step to be addressed in this process. Various classes of vehicles are to be detected from the surveillance video and then they need to be classified based on various feature points. This paper brings out the different methods used for the vehicle detection from a video. An overview of the edge detection methodology is also given here, which is one of the methodologies used in vehicle detection.

show abstract

“…Edges: Vehicle features such as silhouettes, bumpers, rear windows, and license plates exhibit strong linear textures in both vertical and horizontal directions [44]. Extracting these typical edge features from the image allows for a further determination of the car's bounding box [45,46].…”

mentioning

confidence: 99%

Vehicle Detection Algorithms for Autonomous Driving: A Review

Liang,

Ma,

Zhao

et al. 2024

Sensors

View full text Add to dashboard Cite

Autonomous driving, as a pivotal technology in modern transportation, is progressively transforming the modalities of human mobility. In this domain, vehicle detection is a significant research direction that involves the intersection of multiple disciplines, including sensor technology and computer vision. In recent years, many excellent vehicle detection methods have been reported, but few studies have focused on summarizing and analyzing these algorithms. This work provides a comprehensive review of existing vehicle detection algorithms and discusses their practical applications in the field of autonomous driving. First, we provide a brief description of the tasks, evaluation metrics, and datasets for vehicle detection. Second, more than 200 classical and latest vehicle detection algorithms are summarized in detail, including those based on machine vision, LiDAR, millimeter-wave radar, and sensor fusion. Finally, this article discusses the strengths and limitations of different algorithms and sensors, and proposes future trends.

show abstract

Edge-based forward vehicle detection method for complex scenes

Cited by 7 publications

References 5 publications

Shadow-Based Vehicle Detection in Urban Traffic

Shadow-Based Vehicle Detection in Urban Traffic

Application of Edge Detection for Vehicle Detection in Traffic Surveillance System

Vehicle Detection Algorithms for Autonomous Driving: A Review

Contact Info

Product

Resources

About