Passive vision road obstacle detection: a literature mapping

Rateke, Thiago; Wangenheim, Aldo von

doi:10.1080/1206212x.2020.1758877

Cited by 10 publications

(10 citation statements)

References 162 publications

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“…The global market share of vehicles with autonomous driving function will reach or even exceed 25%. In addition, for our country, the huge market demand, the amazing car sales, and the strong consumer demand for high technology will make China expected to be the largest market for smart vehicles worldwide at some point in the future [16].…”

Section: Research Methods and Basic Theorymentioning

confidence: 99%

Intelligent Vehicle Visual Navigation Algorithm Based on Visual Saliency Improvement

Chen

2022

Journal of Sensors

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The automobile has gradually developed into an indispensable tool for human daily travel and transportation. Further reducing the traffic accident rate to improve the traffic safety level and improving the road traffic safety performance is a global issue worthy of common concern for human beings, moreover, it is a common concern for political circles, scholars, researchers, and other related workers in the field of transportation all over the world. Therefore, in this paper, by studying a large amount of literature and carrying out relevant model construction, based on the theories of visual navigation basic theory, intelligent vehicle theory and visual saliency improvement theory, etc., the intelligent vehicle visual navigation with visual saliency improvement is studied in depth through the feature point tracking algorithm research method (including three feature point methods and one optical flow method), and it is concluded that the intelligent vehicle with visual saliency improvement is better than the ordinary. The conclusion that the overall performance of the vehicle is better in all aspects. The following discussions are also proposed for the algorithm improvement: acquisition of visual saliency images; continuous enhancement of visual saliency of images; reasonable application of filtering algorithm.

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Section: Research Methods and Basic Theorymentioning

confidence: 99%

Intelligent Vehicle Visual Navigation Algorithm Based on Visual Saliency Improvement

Chen

2022

Journal of Sensors

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“…There are a number of CNN-based studies in the literature, such as those focused on automatic license plate recognition [ 40 , 41 ], traffic sign detection and recognition [ 25 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ], vehicle detection [ 52 , 53 , 54 , 55 ], pedestrian detection [ 56 , 57 , 58 , 59 , 60 ], lane line detection [ 61 , 62 , 63 ], obstacle detection [ 64 ], video anomaly detection [ 65 , 66 , 67 , 68 ], structural damage detection [ 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 ], and steering angle detection [ 79 , 80 , 81 , 82 ] in autonomous vehicles. The most popular and advanced CNN-based architectures in the literature [ 83 , 84 ] are presented in Figure 3 .…”

Section: Computer Vision Studies In the Field Of Itsmentioning

confidence: 99%

“…However, this can result in a significant number of false-positive detection alarms, or in systems missing obstacles that need to be detected. For this reason, different types of sensors that also provide environmental sensing, such as LIDAR sensors, are used in obstacle detection [ 83 ].…”

Section: Computer Vision Applications In Intelligent Transportation S...mentioning

confidence: 99%

“…Recently, [ 83 ] presented a literature-mapping study focusing on CV techniques that use cameras for obstacle detection in smart navigation systems. In this study, the authors analyzed approaches based on (i) image segmentation (IS), (ii) stereo vision (SV), (iii) optical flow (OF), and (iv) neural networks.…”

Section: Computer Vision Applications In Intelligent Transportation S...mentioning

confidence: 99%

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Computer Vision Applications in Intelligent Transportation Systems: A Survey

Dilek

Dener

2023

Sensors

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As technology continues to develop, computer vision (CV) applications are becoming increasingly widespread in the intelligent transportation systems (ITS) context. These applications are developed to improve the efficiency of transportation systems, increase their level of intelligence, and enhance traffic safety. Advances in CV play an important role in solving problems in the fields of traffic monitoring and control, incident detection and management, road usage pricing, and road condition monitoring, among many others, by providing more effective methods. This survey examines CV applications in the literature, the machine learning and deep learning methods used in ITS applications, the applicability of computer vision applications in ITS contexts, the advantages these technologies offer and the difficulties they present, and future research areas and trends, with the goal of increasing the effectiveness, efficiency, and safety level of ITS. The present review, which brings together research from various sources, aims to show how computer vision techniques can help transportation systems to become smarter by presenting a holistic picture of the literature on different CV applications in the ITS context.

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“…The state-of-the-art for obstacle detection is already quite robust, and with the recent advancements in convolutional neural network (CNN)-based deep learning approaches, has been obtaining excellent results. Prior to this work, we performed a Systematic Literature Review (Rateke and von Wangenheim (2018) and Rateke and von Wangenheim (2020)), based on the procedures described in Kitchenham and Charters (2007). And based on this literature review we were able to determine that the state-of-the-art in the road obstacle detection area with focus on vehicular navigation has many examples and different approaches.…”

Section: Introductionmentioning

confidence: 99%

Road obstacles positional and dynamic features extraction combining object detection, stereo disparity maps and optical flow data

Rateke,

von Wangenheim

2020

Preprint

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One of the most relevant tasks in an intelligent vehicle navigation system is the detection of obstacles. It is important that a visual perception system for navigation purposes identifies obstacles, and it is also important that this system can extract essential information that may influence the vehicle's behavior, whether it will be generating an alert for a human driver or guide an autonomous vehicle in order to be able to make its driving decisions. In this paper we present an approach for the identification of obstacles and extraction of class, position, depth and motion information from these objects that employs data gained exclusively from passive vision. We performed our experiments on two different data-sets and the results obtained shown a good efficacy from the use of depth and motion patterns to assess the obstacles' potential threat status.

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Passive vision road obstacle detection: a literature mapping

Cited by 10 publications

References 162 publications

Intelligent Vehicle Visual Navigation Algorithm Based on Visual Saliency Improvement

Intelligent Vehicle Visual Navigation Algorithm Based on Visual Saliency Improvement

Computer Vision Applications in Intelligent Transportation Systems: A Survey

Road obstacles positional and dynamic features extraction combining object detection, stereo disparity maps and optical flow data

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