Real-time video analysis for vehicle lights detection using temporal information

Eichner, Marcin; Breckon, Toby P.

doi:10.1049/cp:20070047

Cited by 13 publications

(10 citation statements)

References 2 publications

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“…shadow under vehicle [35], [36], [82], edges [83], corners [34], symmetry [84]- [89], texture [90], color [91] and lights of vehicle [92], [93]. a) Shadow Feature: Shadow under vehicle provides a cue for vehicle existence since it is darker than the adjoining road surface.…”

Section: ) Motion Based Approachmentioning

confidence: 99%

“…Appearance features had been applied successfully in vehicle detection systems [92], [93], [96] for car, truck, jeep etc., but in case of motorcycles, all of them failed to provide promising results. This is because it's small size which causes the measurement of symmetry property very difficult and not to provide actual ROI location.…”

Section: F Analysis On Motorcycle Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Vehicle Detection Techniques for Collision Avoidance Systems: A Review

Mukhtar

Xia

Tang

2015

IEEE Trans. Intell. Transport. Syst.

385

202

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Over the past decade, vision-based vehicle detection techniques for road safety improvement have gained an increasing amount of attention. Unfortunately, the techniques suffer from robustness due to huge variability in vehicle shape (particularly for motorcycles), cluttered environment, various illumination conditions, and driving behavior. In this paper, we provide a comprehensive survey in a systematic approach about the state-of-the-art on-road vision-based vehicle detection and tracking systems for collision avoidance systems (CASs). This paper is structured based on a vehicle detection processes starting from sensor selection to vehicle detection and tracking. Techniques in each process/step are reviewed and analyzed individually. Two main contributions in this paper are the following: survey on motorcycle detection techniques and the sensor comparison in terms of cost and range parameters. Finally, the survey provides an optimal choice with a low cost and reliable CAS design in vehicle industries.

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Section: ) Motion Based Approachmentioning

confidence: 99%

Section: F Analysis On Motorcycle Detectionmentioning

confidence: 99%

Vehicle Detection Techniques for Collision Avoidance Systems: A Review

Mukhtar

Xia

Tang

2015

IEEE Trans. Intell. Transport. Syst.

385

202

View full text Add to dashboard Cite

show abstract

“…Furthermore, as the range of automotive applications such as speed sign detection [3], road feature detection [4] headlight detection [5] and road marking recognition [6] are becoming more prevalent in the automotive industry, all-weather operation is becoming a key topic for future vehicle autonomy. In addition, the impact of adverse weather conditions notably affects other common visual surveillance tasks [7,8].…”

Section: Introductionmentioning

confidence: 99%

Improved raindrop detection using combined shape and saliency descriptors with scene context isolation

Webster

Breckon

2015

2015 IEEE International Conference on Image Processing (ICIP)

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Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. ABSTRACTThe presence of raindrop induced image distortion has a significant negative impact on the performance of a wide range of all-weather visual sensing applications including within the increasingly import contexts of visual surveillance and vehicle autonomy. A key part of this problem is robust raindrop detection such that the potential for performance degradation in effected image regions can be identified. Here we address the problem of raindrop detection in colour video imagery using an extended feature descriptor comprising localised shape, saliency and texture information isolated from the overall scene context. This is verified within a bag of visual words feature encoding framework using Support Vector Machine and Random Forest classification to achieve notable 86% detection accuracy with minimal false positives compared to prior work. Our approach is evaluated under a range of environmental conditions typical of all-weather automotive visual sensing applications.

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“…As such, monitoring driver gaze through a combination of visual head pose estimation and tracking has many applications in future driver assistance [1,2,3,4] and intelligent vehicle safety systems [5,6,7] -ranging from collision detection through to drowsiness alerting [8]. For general usage within this environment, approaches are required to be both driver invariant and robust to the highly variant illumination conditions of an in-transit vehicle interior.…”

Section: Introductionmentioning

confidence: 99%

A comparison of features for regression-based driver head pose estimation under varying illumination conditions

Walger

Breckon

Gaszczak

et al. 2014

2014 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM)

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Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. ABSTRACTHead pose estimation provides key information about driver activity and awareness. Prior comparative studies are limited to temporally consistent illumination conditions under the assumption of brightness constancy. By contrast the illumination conditions inside a moving vehicle vary considerably with environmental conditions. In this study we present a base comparison of three features for head pose estimation, via support vector machine regression, based on Histogram of Oriented Gradient (HOG) features, Gabor filter responses and Active Shape Model (ASM) landmark features. These, reputedly illumination invariant, are presented through a common face localization framework from which we estimate driver head pose in two degrees-of-freedom and compare against a baseline approach for recovering head pose via weak perspective geometry. Evaluation is performed over a number of invehicle sequences, exhibiting uncontrolled illumination variation, in addition to ground truth data-sets, with controlled illumination changes, upon which we achieve a minimal~12°a nd~15°mean error in pitch and yaw respectively via ASM landmark features.

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Real-time video analysis for vehicle lights detection using temporal information

Abstract: We have proposed a real-time system for vehicle lights detection. This goal has been achieved by combining together a rule-based approach and a temporal tracking system.

Cited by 13 publications

References 2 publications

Vehicle Detection Techniques for Collision Avoidance Systems: A Review

Vehicle Detection Techniques for Collision Avoidance Systems: A Review

Improved raindrop detection using combined shape and saliency descriptors with scene context isolation

A comparison of features for regression-based driver head pose estimation under varying illumination conditions

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