The reliable detection of speed of moving vehicles is considered key to traffic law enforcement in most countries, and is seen by many as an important tool to reduce the number of traffic accidents and fatalities. Many automatic systems and different methods are employed in different countries, but as a rule they tend to be expensive and/or labor intensive, often employing outdated technology due to the long development time. Here we describe a speed detection system that relies on simple everyday equipment - a laptop and a consumer web camera. Our method is based on tracking the license plates of cars, which gives the relative movement of the cars in the image. This image displacement is translated to actual motion by using the method of projection to a reference plane, where the reference plane is the road itself. However, since license plates do not touch the road, we must compensate for the entailed distortion in speed measurement. We show how to compute the compensation factor using knowledge of the license plate standard dimensions. Consequently our system computes the true speed of moving vehicles fast and accurately. We show promising results on videos obtained in a number of scenes and with different car models.
CVVLSNet: Vehicle Location and Speed Estimation Using Partial Connected Vehicle Trajectory Data
Tsinghua University（清华大学）；The University of Hong Kong（香港大学）；Nanjing University of Science and Technology（南京理工大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Speed estimation of an ego vehicle is crucial to enable autonomous driving and advanced driver assistance technologies. Due to functional and legacy issues, conventional methods depend on in-car sensors to extract vehicle speed through the Controller Area Network bus. However, it is desirable to have modular systems that are not susceptible to external sensors to execute perception tasks. In this paper, we propose a novel 3D-CNN with masked-attention architecture to estimate ego vehicle speed using a single front-facing monocular camera. To demonstrate the effectiveness of our method, we condu…
Peking University（北京大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；University of Sydney（悉尼大学）
Light-weight convolutional neural networks (CNNs) are specially designed for applications on mobile devices with faster inference speed. The convolutional operation can only capture local information in a window region, which prevents performance from being further improved. Introducing self-attention into convolution can capture global information well, but it will largely encumber the actual speed. In this paper, we propose a hardware-friendly attention mechanism (dubbed DFC attention) and then present a new GhostNetV2 architecture for mobile applications. The proposed DFC attention is const…
Vehicle speed dataset for the major European road network derived from Sentinel-2 imagery, 2022-2026
Heidelberg Institute for Geoinformation Technology (HeiGIT)（海德堡地理信息技术研究所）；Faculty of Geographical Sciences, University of Lodz（罗兹大学地理科学学院）；GIScience Research Group, Institute of Geography, University of Heidelberg（海德堡大学地理研究所地理信息科学研究组）
Acquiring data to train deep learning-based object detectors on Unmanned Aerial Vehicles (UAVs) is expensive, time-consuming and may even be prohibited by law in specific environments. On the other hand, synthetic data is fast and cheap to access. In this work, we explore the potential use of synthetic data in object detection from UAVs across various application environments. For that, we extend the open-source framework DeepGTAV to work for UAV scenarios. We capture various large-scale high-resolution synthetic data sets in several domains to demonstrate their use in real-world object detect…
Chittagong University of Engineering and Technology（吉大港工程技术大学）
Wrong-way driving is one of the main causes of road accidents and traffic jam all over the world. By detecting wrong-way vehicles, the number of accidents can be minimized and traffic jam can be reduced. With the increasing popularity of real-time traffic management systems and due to the availability of cheaper cameras, the surveillance video has become a big source of data. In this paper, we propose an automatic wrong-way vehicle detection system from on-road surveillance camera footage. Our system works in three stages: the detection of vehicles from the video frame by using the You Only Lo…
Enhanced Vehicle Speed Detection Considering Lane Recognition Using Drone Videos in California
Department of Computer Science, University of California, Davis（计算机科学系，加州大学戴维斯分校）；Department of Civil and Environmental Engineering, University of California, Davis（土木与环境工程系，加州大学戴维斯分校）
Detection and tracking of vehicles captured by traffic surveillance cameras is a key component of intelligent transportation systems. We present an improved version of our algorithm for detection of 3D bounding boxes of vehicles, their tracking and subsequent speed estimation. Our algorithm utilizes the known geometry of vanishing points in the surveilled scene to construct a perspective transformation. The transformation enables an intuitive simplification of the problem of detecting 3D bounding boxes to detection of 2D bounding boxes with one additional parameter using a standard 2D object d…
The lane-level localization accuracy is very important for autonomous vehicles. The Global Navigation Satellite System (GNSS), e.g. GPS, is a generic localization method for vehicles, but is vulnerable to the multi-path interference in the urban environment. Integrating the vision-based relative localization result and a digital map with the GNSS is a common and cheap way to increase the global localization accuracy and thus to realize the lane-level localization. This project is to develop a mono-camera based lane-changing behavior detection module for the correction of lateral GPS localizati…
University of Alcalá（阿尔卡拉大学）；European Commission, Joint Research Center（欧盟委员会联合研究中心）
Despite all the challenges and limitations, vision-based vehicle speed detection is gaining research interest due to its great potential benefits such as cost reduction, and enhanced additional functions. As stated in a recent survey [1], the use of learning-based approaches to address this problem is still in its infancy. One of the main difficulties is the need for a large amount of data, which must contain the input sequences and, more importantly, the output values corresponding to the actual speed of the vehicles. Data collection in this context requires a complex and costly setup to capt…
University of Rochester（罗切斯特大学）；SAIC USA Innovation Center（上汽美国创新中心）
Convolutional Neural Networks (CNN) have been successfully applied to autonomous driving tasks, many in an end-to-end manner. Previous end-to-end steering control methods take an image or an image sequence as the input and directly predict the steering angle with CNN. Although single task learning on steering angles has reported good performances, the steering angle alone is not sufficient for vehicle control. In this work, we propose a multi-task learning framework to predict the steering angle and speed control simultaneously in an end-to-end manner. Since it is nontrivial to predict accurat…
Virginia Polytechnic Institute and State University（弗吉尼亚理工学院暨州立大学）
In a previous study, we presented VT-Lane, a three-step framework for real-time vehicle detection, tracking, and turn movement classification at urban intersections. In this study, we present a case study incorporating the highly accurate trajectories and movement classification obtained via VT-Lane for the purpose of speed estimation and driver behavior calibration for traffic at urban intersections. First, we use a highly instrumented vehicle to verify the estimated speeds obtained from video inference. The results of the speed validation show that our method can estimate the average travel …
Faculty of Electrical Engineering University of Montenegro Podgorica, Montenegro；Faculty of Maritime Studies University of Montenegro Kotor, Montenegro
Accurate speed estimation of road vehicles is important for several reasons. One is speed limit enforcement, which represents a crucial tool in decreasing traffic accidents and fatalities. Compared with other research areas and domains, the number of available datasets for vehicle speed estimation is still very limited. We present a dataset of on-road audio-video recordings of single vehicles passing by a camera at known speeds, maintained stable by the on-board cruise control. The dataset contains thirteen vehicles, selected to be as diverse as possible in terms of manufacturer, production ye…
University of Alcalá（阿尔卡拉大学）；European Commission, Joint Research Center（欧盟委员会联合研究中心）
The need to accurately estimate the speed of road vehicles is becoming increasingly important for at least two main reasons. First, the number of speed cameras installed worldwide has been growing in recent years, as the introduction and enforcement of appropriate speed limits is considered one of the most effective means to increase the road safety. Second, traffic monitoring and forecasting in road networks plays a fundamental role to enhance traffic, emissions and energy consumption in smart cities, being the speed of the vehicles one of the most relevant parameters of the traffic state. Am…
University of Alcalá（阿尔卡拉大学）；European Commission, Joint Research Centre (JRC)（欧盟委员会联合研究中心）
The use of cameras for vehicle speed measurement is much more cost effective compared to other technologies such as inductive loops, radar or laser. However, accurate speed measurement remains a challenge due to the inherent limitations of cameras to provide accurate range estimates. In addition, classical vision-based methods are very sensitive to extrinsic calibration between the camera and the road. In this context, the use of data-driven approaches appears as an interesting alternative. However, data collection requires a complex and costly setup to record videos under real traffic conditi…
University of Engineering and Technology Peshawar（白沙瓦工程技术大学）；University of Swabi（斯瓦比大学）；COMSATS University Islamabad（伊斯兰堡COMSATS大学）；Jessore University of Science and Technology（杰索尔科技大学）
Vehicle speed monitoring and management of highways is the critical problem of the road in this modern age of growing technology and population. A poor management results in frequent traffic jam, traffic rules violation and fatal road accidents. Using traditional techniques of RADAR, LIDAR and LASAR to address this problem is time-consuming, expensive and tedious. This paper presents an efficient framework to produce a simple, cost efficient and intelligent system for vehicle speed monitoring. The proposed method uses an HD (High Definition) camera mounted on the road side either on a pole or …
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Army Research Laboratory（陆军研究实验室）
One of the key challenges in high speed off road navigation on ground vehicles is that the kinodynamics of the vehicle terrain interaction can differ dramatically depending on the terrain. Previous approaches to addressing this challenge have considered learning an inverse kinodynamics (IKD) model, conditioned on inertial information of the vehicle to sense the kinodynamic interactions. In this paper, we hypothesize that to enable accurate high-speed off-road navigation using a learned IKD model, in addition to inertial information from the past, one must also anticipate the kinodynamic intera…
Beijing University of Posts and Telecommunications（北京邮电大学）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）
This paper investigates the localization problem of high-speed high-altitude unmanned aerial vehicle (UAV) with a monocular camera and inertial navigation system. It proposes a navigation method utilizing the complementarity of vision and inertial devices to overcome the singularity which arises from the horizontal flight of UAV. Furthermore, it modifies the mathematical model of localization problem via separating linear parts from nonlinear parts and replaces a nonlinear least-squares problem with a linearly equality-constrained optimization problem. In order to avoid the ill-condition prope…
