Abstract
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.
Highlights
In recent years, many countries around the world have been facing road traffic issues such as accidents, congestion, and pollution
Since several pedestrian and vehicle detection algorithms made use of the same or a modified version of the methods used in generic object detection algorithms, this paper reviews relevant generic object detection algorithms
Nowadays, the preferred methods for object detection are based on Deep Learning (DL) techniques;
Summary
Many countries around the world have been facing road traffic issues such as accidents, congestion, and pollution. According to WHO [1], in 2016, the number of fatalities due to road traffic accidents reached 1.35 million, and approximately 20 to 50 million people are injured each year. In order to decrease road traffic accidents and fatalities, the following measures were presented: enforce legislation to avoid human error and imprudence, improve vehicle safety to avoid or mitigate collisions, and post-crash care to increase the chance of saving lives. Strategies have been proposed to reduce congestion and pollution, for example, making road improvements and using other methods of transportation (e.g., cycling, trains, buses, etc.); it is expected that by 2050 the urban population will double [4] and, in the twelve years, the number of cars on the road will be approximately two billion [5]. The subsequent review papers will review the computer vision techniques used to detect and recognise traffic signs and traffic lights, and the technique used to predict the non-static objects behaviour
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
