Pedestrian and Vehicle Detection in Autonomous Vehicle Perception Systems—A Review

Autonomous Vehicles (AVs) have the potential to solve many traffic problems, such as accidents, congestion and pollution. However, there are still challenges to overcome, for instance, AVs need to accurately perceive their environment to safely navigate in busy urban scenarios. The aim of this paper is to review recent articles on computer vision techniques that can be used to build an AV perception system. AV perception systems need to accurately detect non-static objects and predict their behaviour, as well as to detect static objects and recognise the information they are providing. This paper, in particular, focuses on the computer vision techniques used to detect pedestrians and vehicles. There have been many papers and reviews on pedestrians and vehicles detection so far. However, most of the past papers only reviewed pedestrian or vehicle detection separately. This review aims to present an overview of the AV systems in general, and then review and investigate several detection computer vision techniques for pedestrians and vehicles. The review concludes that both traditional and Deep Learning (DL) techniques have been used for pedestrian and vehicle detection; however, DL techniques have shown the best results. Although good detection results have been achieved for pedestrians and vehicles, the current algorithms still struggle to detect small, occluded, and truncated objects. In addition, there is limited research on how to improve detection performance in difficult light and weather conditions. Most of the algorithms have been tested on well-recognised datasets such as Caltech and KITTI; however, these datasets have their own limitations. Therefore, this paper recommends that future works should be implemented on more new challenging datasets, such as PIE and BDD100K.EPSRC DTP PhD studentshi

Macrofeature layout selection for pedestrian localization and its acceleration using GPU

Macrofeatures are mid-level features that jointly encode a set of low-level features in a neighborhood. We propose a macrofeature layout selection technique to improve localization performance in an object detection task. Our method employs line, triangle, and pyramid layouts, which are composed of several local blocks represented by the Histograms of Oriented Gradients (HOGs) features in a multi-scale feature pyramid. Such macrofeature layouts are integrated into a boosting framework for object detection, where the best layout is selected to build a weak classifier in a greedy manner at each iteration. The proposed algorithm is applied to pedestrian detection and implemented using GPU. Our pedestrian detection algorithm performs better in terms of detection and localization accuracy with great efficiency when compared to several state-of-the-art techniques in public datasets. (C) 2013 Elsevier Inc. All rights reserved.X1111sciescopu