Lane-Change Decision Aid System Based on Motion-Driven Vehicle Tracking

Díaz, Javier; Vidal, Eduardo Ros; Rotter, Alexander; Mühlenberg, Martin

doi:10.1109/tvt.2008.917220

Cited by 112 publications

(53 citation statements)

References 25 publications

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“…Detecting vehicles with this camera placement presents difficulty because of the field of view and high variability in the appearance of vehicles, depending on their relative positions. In [16], this camera placement was used to detect overtaking vehicles using optical flow. In the absence of a vehicle in the blind spot, the optical flow of static objects moves backward with respect to the ego vehicle.…”

Section: A Monocular Vehicle Detectionmentioning

confidence: 99%

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Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Sivaraman

Trivedi

2013

IEEE Trans. Intell. Transport. Syst.

802

322

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Abstract-This paper provides a review of the literature in on-road vision-based vehicle detection, tracking, and behavior understanding. Over the past decade, vision-based surround perception has progressed from its infancy into maturity. We provide a survey of recent works in the literature, placing vision-based vehicle detection in the context of sensor-based on-road surround analysis. We detail advances in vehicle detection, discussing monocular, stereo vision, and active sensor-vision fusion for on-road vehicle detection. We discuss vision-based vehicle tracking in the monocular and stereo-vision domains, analyzing filtering, estimation, and dynamical models. We discuss the nascent branch of intelligent vehicles research concerned with utilizing spatiotemporal measurements, trajectories, and various features to characterize on-road behavior. We provide a discussion on the state of the art, detail common performance metrics and benchmarks, and provide perspective on future research directions in the field.

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Section: A Monocular Vehicle Detectionmentioning

confidence: 99%

“…Oncoming vehicles exhibit forward flow. Vehicles were tracked using Kalman filtering [16]. Optical flow was also used for blind spot detection in [17].…”

Section: A Monocular Vehicle Detectionmentioning

confidence: 99%

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Sivaraman

Trivedi

2013

IEEE Trans. Intell. Transport. Syst.

802

322

View full text Add to dashboard Cite

show abstract

“…Ego-motion estimation using optical flow, and integrated detection of vehicles was implemented in [3]. In [27], optical flow was used to detect overtaking vehicles in the blind spot.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for On-road Vehicle Detection and Tracking

Jaafari¹,

Ansari²,

Koutti³

et al. 2016

ijacsa

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Abstract-On the basis of a necessary development of the road safety, vision-based vehicle detection techniques have gained an important amount of attention. This work presents a novel vehicle detection and tracking approach, and structured based on a vehicle detection process starting from, images or video data acquired from sensors installed on board of the vehicle, to vehicle detection and tracking. The features of the vehicle are extracted by the proposed GIST image processing algorithm, and recognized by the state-of-art Support Vectors Machine classifier. The tracking process was performed based on edge features matching approach. The Kalman filter was used to correct the measurements. Extensive experiments were carried out on real image data validate that it is promising to employ the proposed approach for on road vehicle detection and tracking.

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“…The disadvantages of this system are the high cost of the stereoscopic visual sensors and the time-consuming calculations. Alonso et al (13) have proposed a safety assistant system for dynamically monitoring overtaking from the rear of a vehicle. Rear road status information from side mirror cameras mounted outside the vehicle is acquired, and calculations are made of the dynamic driving status using the Lucas-Kanade optical flow method.…”

Section: Introductionmentioning

confidence: 99%

Fast Estimation of Pedestrian Movement

Kuo¹,

Cheng‐Tao²,

Chih‐Hao³

2017

Sensors and Materials

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In this study, a single camera has been used to capture images of the road in front of a moving vehicle. Image processing algorithms identify the location of pedestrians in the images, calculate the direction and rate of movement of each one, and issue safety warnings. The pedestrian motion vector detection and warning system presented here has three components: The first serves to identify, locate, and mark the positions of pedestrians in images using the Fisher classifier. The follow-up image processing is confined to the labeled images, and this significantly reduces the image postprocessing load. The second component involves the calculation of pedestrian motion vectors using the Lucas-Kanade optical flow method. Finally, the vehicle's future zone of movement is established, and judgment is made as to whether any pedestrians will be in this zone or not. This is determined from the pedestrian movement vectors and a warning can be triggered to alert the driver in time for any necessary avoidance action to be taken. This can improve road safety and reduce the number of accidents involving pedestrians, which result from driver fatigue, negligence or careless driving, and the number of such accidents can be reduced.

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Lane-Change Decision Aid System Based on Motion-Driven Vehicle Tracking

Cited by 112 publications

References 25 publications

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

A Novel Approach for On-road Vehicle Detection and Tracking

Fast Estimation of Pedestrian Movement

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