Towards a closer fusion of active and passive safety: Optical flow-based detection of vehicle side collisions

Michalke, Thomas; Stein, Fridtjof; Franke, Uwe

doi:10.1109/ivs.2011.5940446

Cited by 12 publications

(6 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Stereo-based systems [2,3,4] involve finding correspondences between the left and right images of the stereo image pair of a scene which is a complex and time-consuming task. Motion-based methods exploit the optical flow of moving vehicles obtained by matching pixels from consecutives frames of an image sequence [5,6]. The computational cost of this method is expensive and requires the processing of several frames to detect a vehicle.…”

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

“…Sparse motion analysis is designed for having accurate motion vectors at a few selected pixel locations. Dense motion analysis is suitable for detecting short displacements (known as optical flow calculation) (21), and sparse motion analysis can also be designed for detecting large displacements (22). Motion analysis is a difficult twodimensional correspondence problem, and solutions might become easier by having recorded high-resolution images at a higher frame rate in future.…”

Section: Motion Computationmentioning

confidence: 99%

Vision‐Based Driver Assistance

Klette

2015

Wiley Encyclopedia of Electrical and Electronics Engineering

View full text Add to dashboard Cite

Vision‐based driver assistance systems (VB‐DAS) still assume a driver in the ego‐vehicle (i.e., that vehicle where the system operates in); autonomous driving can benefit from solutions designed for VB‐DAS. The article reviews research subjects for VB‐DAS and provides an extensive bibliography. It starts with defining basic concepts and notations in the field, also highlighting the importance of performance evaluation for VB‐DAS solutions. The article then reviews briefly safety and comfort functionalities (avoidance of blind spots, night vision, and virtual windshield), before discussing in detail solutions for basic and midlevel environment perception. This involves distance and motion computation, ego‐motion understanding, obstacle detection, tasks of object tracking (vehicles or pedestrians), and the detection and recognition of infrastructure key elements (road, lane, traffic signs, and free space). Representative VB‐DAS examples (e.g., driver monitoring and driver‐environment understanding, speed adaptation, queuing, parking, blind spot supervision, lane departure warning, wrong lane detection, intelligent headlamp control, or intercar communication) are also described. Outlook toward autonomous driving and road‐environment modeling conclude the article.

show abstract

“…Regarding the vehicle detection, most of the monovision approaches developed are based on the search for specific patterns using information about the vehicles’ features such as symmetry [19], texture [20], colour [21], edges [22], corners [23] and motion features [24]. These approaches have been demonstrated to be effective under a specific environment, but they are very dependent on illumination.…”

Section: Related Workmentioning

confidence: 99%

Monovision‐based vehicle detection, distance and relative speed measurement in urban traffic

Arenado

Oria

Torre-Ferrero

et al. 2014

IET Intelligent Transport Systems

View full text Add to dashboard Cite

Abstract-This paper presents a monovision-based system for on-road vehicle detection and computation of distance and relative speed in urban traffic. Many works have dealt with monovision vehicle detection, but only a few of them provide the distance to the vehicle which is essential for the control of an Intelligent Transportation System (ITS). The system proposed integrates a single camera reducing the monetary cost of stereovision and RADAR-based technologies. The algorithm is divided in three major stages. For vehicle detection we use a combination of two features: the shadow underneath the vehicle and horizontal edges. We propose a new method for shadow thresholding based on the grayscale histogram assessment of a region of interest on the road. In the second and third stages, the vehicle hypothesis verification and the distance are obtained by means of its number plate whose dimensions and shape are standardized in each country. The analysis of consecutive frames is employed to calculate the relative speed of the vehicle detected. Experimental results showed excellent performance in both vehicle and number plate detections and in the distance measurement, in terms of accuracy and robustness in complex traffic scenarios and under different lighting conditions.

show abstract

Towards a closer fusion of active and passive safety: Optical flow-based detection of vehicle side collisions

Cited by 12 publications

References 13 publications

Shadow-Based Vehicle Detection in Urban Traffic

Shadow-Based Vehicle Detection in Urban Traffic

Vision‐Based Driver Assistance

Monovision‐based vehicle detection, distance and relative speed measurement in urban traffic

Contact Info

Product

Resources

About