Analysis of Traffic Scenes by Using the Hierarchical Structure Code

Austermeier, Hubert; Büker, Ulrich; Mertsching, Bärbel; Zimmermann, Stephan

doi:10.1142/9789812797919_0047

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…Experiments showed that a robust recognition of traffic signs is possible up to a distance of 30 meters using our equipment. Detailed recognition results can be found in [2]. After parallelizing the algorithms When analyzing the runtime, some points have to be considered:…”

Section: Resultsmentioning

confidence: 99%

Parallel Evaluation of Hierarchical Image Databases

Büker

Mertsching

1995

Journal of Parallel and Distributed Computing

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Section: Resultsmentioning

confidence: 99%

Parallel Evaluation of Hierarchical Image Databases

Büker

Mertsching

1995

Journal of Parallel and Distributed Computing

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“…Among those who work directly with a black and white image are Austerirmeier et al [5], Besserer et al [8] and Buker and Mertsching [10] who detect the borders in a pyramidal structure. They do not take occlusions into account.…”

Section: Sign Detectionmentioning

confidence: 99%

Traffic sign recognition and analysis for intelligent vehicles

Escalera

Armingol

Mata

2003

Image and Vision Computing

385

182

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This paper deals with object recognition in outdoor environments. In this type of environments, lighting conditions cannot be controlled and predicted, objects can be partially occluded, and their position and orientation is not known a priori. The chosen type of objects is traffic or road signs, due to their usefulness for sign maintenance, inventory in highways and cities, Driver Support Systems and Intelligent Autonomous Vehicles. A genetic algorithm is used for the detection step, allowing an invariance localisation to changes in position, scale, rotation, weather conditions, partial occlusion, and the presence of other objects of the same colour. A neural network achieves the classification. The global system not only recognises the traffic sign but also provides information about its condition or state. q

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“…However there are some researchers who prefer not to use it. In those implementations where grayscale images are preferred, the TRD is based mainly on morphology features, such as symmetry [54], [55], [56], distance transformations from offline generated templates [57] and pyramidal structures for border detection [58], [59], [60]. A machine learning approach to TSR using genetic algorithms and neural networks is proposed in [61].…”

Section: Related Workmentioning

confidence: 99%

Advanced driver assistance systems with computer vision methods

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Traffic accidents are one of the main reasons for the loss of human lives worldwide. Their increasing number has led to the realization that the use of advanced technology for manufacturing safer vehicles is imperative for limiting casualties. Since technological breakthroughs allowed the incorporation of cheap, low consumption systems with high processing speeds in vehicles, it became apparent that complex computer vision techniques could be used to assist drivers in navigating their vehicles. In this direction, this thesis focuses on providing novel solutions for different tasks involved in advanced driver assistance systems. More specifically, this thesis proposes novel subsystems for traffic sign recognition, traffic light recognition, preceding vehicle detection and road detection. The techniques used for developing the proposed solutions are based on color image processing with a focus on illumination invariance, using symmetry information for man-made objects (like traffic signs, traffic lights and vehicles) detection, spatiotemporal tracking of detected results and automated image segmentation for road detection. The proposed systems were implemented with a goal of robustness to changes of illumination and weather conditions, as well as to diverse driving environments. A special focus on the prospect for real-time implementation has also been given. The results presented in this thesis indicate the superiority of the proposed methods to their counterparts found in relevant literature in both normal and challenging conditions, especially in the cases of preceding vehicle detection and road detection. Hopefully, parts of this research will provide new insights for future developments in the field of intelligent transportation.

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Analysis of Traffic Scenes by Using the Hierarchical Structure Code

Cited by 7 publications

References 10 publications

Parallel Evaluation of Hierarchical Image Databases

Parallel Evaluation of Hierarchical Image Databases

Traffic sign recognition and analysis for intelligent vehicles

Advanced driver assistance systems with computer vision methods

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