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

Automatic Multi-Scale and Multi-Object Pedestrian and Car Detection in Digital Images Based on the Discriminative Generalized Hough Transform and Deep Convolutional Neural Networks

Many approaches have been suggested for automatic pedestrian and car detection to cope with the large variability regarding object size, occlusion, background variability, aspect and so forth. Current state-of-the-art deep learning-based frameworks rely either on a proposal generation mechanism (e.g., "Faster R-CNN") or on the inspection of image quadrants / octants (e.g., "YOLO" or "SSD"), which are then further processed with deep convolutional neural networks (CNN). In this thesis, the Discriminative Generalized Hough Transform (DGHT), which operates on edge images, is analyzed for the application to automatic multi-scale and multi-object pedestrian and car detection in 2D digital images. The analysis motivates to use the DGHT as an efficient proposal generation mechanism, followed by a proposal (bounding box) refinement and proposal acceptance or rejection based on a deep CNN. The impact of the different components of the resulting DGHT object detection pipeline as well as the amount of DGHT training data on the detection performance are analyzed in detail. Due to the low false negative rate and the low number of candidates of the DGHT as well as the high classification accuracy of the CNN, competitive performance to the state-of-the-art in pedestrian and car detection is obtained on the IAIR database with much less generated proposals than other proposal-generating algorithms, being outperformed only by YOLOv2 fine-tuned to IAIR cars. By evaluations on further databases (without retraining or adaptation) the generalization capability of the DGHT object detection pipeline is shown