Guest Editorial Introduction to the Special Issue on Robust and Efficient Vision Techniques for Intelligent Vehicles

Wang, Qi; Bergasa, Luis M.; Alvarez, Jose M. · 2018 · Crossref

DOI: 10.1109/tits.2017.2782498

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Summary

This guest editorial introduces a special issue of the *IEEE Transactions on Intelligent Transportation Systems* focused on robust and efficient vision techniques for intelligent vehicles. The authors highlight that while intelligent vehicles integrate various advanced technologies, vision-based techniques are critical for ensuring safety and performance in diverse driving environments. The primary challenge addressed is the need for algorithms that are both robust against unexpected conditions and efficient enough for real-time application. The special issue explores the application of traditional hand-designed methods and state-of-the-art machine learning techniques across six key areas: tracking, detection, segmentation, classification, feature engineering, and assistance systems. The editorial summarizes eighteen accepted papers organized by topic. In the area of tracking, researchers developed lightweight real-time approaches for resource-constrained devices, quadrangle kernelized correlation filters for scale estimation, and unified frameworks for multi-object tracking in complex scenarios. Detection methods focused on vital dynamic information, including fusion detectors for traffic lights and cascaded segmentation-detection systems for text-based traffic signs, including specific adaptations for Uyghur language text. For scene understanding and segmentation, the papers presented novel architectures such as a siamese fully convolutional network (s-FCN-loc) that integrates RGB images, semantic contours, and location priors for road segmentation. Other approaches formulated road detection as consecutive classification and segmentation tasks to handle terrain diversity, while deep background models were constructed using fully convolutional networks for video surveillance. Target retrieval techniques addressed occlusion through cascaded part-based systems and utilized high-quality binary codes for fast retrieval. In feature engineering, the research expanded beyond standard image data to include point clouds from RGB-D cameras and introduced generic proposal evaluators capable of estimating proposal quality without manual annotations. Finally, an advanced driver assistance system was proposed to provide an immersive 3-D surround view, eliminating visual blind spots. The editors conclude that advancing these robust and efficient vision techniques is essential for achieving practical levels of intelligent vehicle autonomy and fostering deeper cooperation between industry and academia. The editorial acknowledges the contributions of reviewers in enhancing the technical quality of the included papers.

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