An image processing system for driver assistance

Handmann, Uwe; Kalinke, Thomas; Tzomakas, Christos; Werner, Martin; Seelen, Werner von

doi:10.1016/s0262-8856(99)00032-3

Cited by 157 publications

(69 citation statements)

References 15 publications

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“…Bertozzi [7] applied the vertical symmetry of the vehicle to establish templates for matching. Handman [8] used the rectangular with a certain length/width ratio range to verify the hypothesis.…”

Section: Vehicle Detection For Intelligent Vehiclementioning

confidence: 99%

A Review of Vision-Based Vehicle Detection and Tracking Techniques for Intelligent Vehicle

Li¹,

Tian²,

Zhang³

et al. 2015

Advances in Intelligent Systems Research

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Abstract. Vision-based vehicle detection and tracking techniques is of great importance to reduce vehicle collision accidents and increase the driving safety on road. This paper presents a comprehensive review of latest techniques for vehicle detection and tracking. In hypothesis generation of vehicles, motion-based, knowledge-based and stereo-vision based methods are introduced. Hypothesis verification includes template-based, appearance-based and multi-features fusion methods. In addition, three main algorithms are introduced in vehicle tracking. Finally, existing problems and future research directions of this field are summarized.

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Section: Vehicle Detection For Intelligent Vehiclementioning

confidence: 99%

A Review of Vision-Based Vehicle Detection and Tracking Techniques for Intelligent Vehicle

Li¹,

Tian²,

Zhang³

et al. 2015

Advances in Intelligent Systems Research

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“…In particular, many interesting applications require a continuous visual input stream that has to be processed under real-time conditions. For example, vehicle driver assistance systems must observe street scenes [1,2] or the driving person [3] in real-time. Similarly, gesture recognition systems or visual surveillance tools are most useful when they are able to process video material onthe-fly, such as [4,5,6].…”

Section: Introductionmentioning

confidence: 99%

Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components

Lampert

Peters

2010

J Real-Time Image Proc

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We describe RTblob, a high speed vision system that detects objects in cluttered scenes based on their color and shape at a speed of over 800 frames per second. Because the system is available as open-source software and relies only on off-the-shelf PC hardware components, it can provide the basis for multiple application scenarios. As an illustrative example, we show how RTblob can be used in a robotic table tennis scenario to estimate ball trajectories through 3D space simultaneously from four cameras images at a speed of 200 Hz.

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“…Grey scale-based approaches focus on object geometry, whereas color-based techniques allow to prevent false positives detection. Traffic sign recognition is studied for several purposes, like autonomous driving under certain simplified conditions or for assisted driving (Handmann et al, 1998). We focus on the goal of mobile mapping (Casacuberta et al, 2004), as a technique used to compile cartographic information from a mobile system.…”

Section: Introductionmentioning

confidence: 99%

Traffic Sign Classification Using Error Correcting Techniques

Escalera

Radeva

Pujol

2007

Proceedings of the Second International Conference on Computer Vision Theory and Applications

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Abstract:Traffic sign classification is a challenging problem in Computer Vision due to the high variability of sign appearance in uncontrolled environments. Lack of visibility, illumination changes, and partial occlusions are just a few problems. In this paper, we introduce a classification technique for traffic signs recognition by means of Error Correcting Output Codes. Recently, new proposals of coding and decoding strategies for the Error Correcting Output Codes framework have been shown to be very effective in front of multiclass problems. We review the state-of-the-art ECOC strategies and combinations of problem-dependent coding designs and decoding techniques. We apply these approaches to the Mobile Mapping problem. We detect the sign regions by means of Adaboost. The Adaboost in an attentional cascade with the extended set of Haar-like features estimated on the integral shows great performance at the detection step. Then, a spatial normalization using the Hough transform and the fast radial symmetry is done. The model fitting improves the final classification performance by normalizing the sign content. Finally, we classify a wide set of traffic signs types, obtaining high success in adverse conditions.

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An image processing system for driver assistance

Cited by 157 publications

References 15 publications

A Review of Vision-Based Vehicle Detection and Tracking Techniques for Intelligent Vehicle

A Review of Vision-Based Vehicle Detection and Tracking Techniques for Intelligent Vehicle

Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components

Traffic Sign Classification Using Error Correcting Techniques

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