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Journal ArticleDOI

Survey of Pedestrian Detection for Advanced Driver Assistance Systems

TLDR
This work divides the problem of detecting pedestrians from images into different processing steps, each with attached responsibilities, and separates the different proposed methods with respect to each processing stage, favoring a comparative viewpoint.
Abstract
Advanced driver assistance systems (ADASs), and particularly pedestrian protection systems (PPSs), have become an active research area aimed at improving traffic safety. The major challenge of PPSs is the development of reliable on-board pedestrian detection systems. Due to the varying appearance of pedestrians (e.g., different clothes, changing size, aspect ratio, and dynamic shape) and the unstructured environment, it is very difficult to cope with the demanded robustness of this kind of system. Two problems arising in this research area are the lack of public benchmarks and the difficulty in reproducing many of the proposed methods, which makes it difficult to compare the approaches. As a result, surveying the literature by enumerating the proposals one--after-another is not the most useful way to provide a comparative point of view. Accordingly, we present a more convenient strategy to survey the different approaches. We divide the problem of detecting pedestrians from images into different processing steps, each with attached responsibilities. Then, the different proposed methods are analyzed and classified with respect to each processing stage, favoring a comparative viewpoint. Finally, discussion of the important topics is presented, putting special emphasis on the future needs and challenges.

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Citations
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Journal ArticleDOI

Pedestrian Detection: An Evaluation of the State of the Art

TL;DR: An extensive evaluation of the state of the art in a unified framework of monocular pedestrian detection using sixteen pretrained state-of-the-art detectors across six data sets and proposes a refined per-frame evaluation methodology.
Journal ArticleDOI

A survey on vision-based human action recognition

TL;DR: A detailed overview of current advances in vision-based human action recognition is provided, including a discussion of limitations of the state of the art and outline promising directions of research.
Journal ArticleDOI

Deep Learning for Generic Object Detection: A Survey

TL;DR: A comprehensive survey of the recent achievements in this field brought about by deep learning techniques, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics.
Journal ArticleDOI

A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles

TL;DR: In this article, the authors present a survey of the state of the art on planning and control algorithms with particular regard to the urban environment, along with a discussion of their effectiveness.
Journal ArticleDOI

Data-Driven Intelligent Transportation Systems: A Survey

TL;DR: A survey on the development of D2ITS is provided, discussing the functionality of its key components and some deployment issues associated with D2 ITS Future research directions for the developed system are presented.
References
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Journal ArticleDOI

Distinctive Image Features from Scale-Invariant Keypoints

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Proceedings ArticleDOI

Histograms of oriented gradients for human detection

TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
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Neural networks for pattern recognition

TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Proceedings ArticleDOI

Rapid object detection using a boosted cascade of simple features

TL;DR: A machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates and the introduction of a new image representation called the "integral image" which allows the features used by the detector to be computed very quickly.
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