scholarly journals An Adaptive Framework for Multi-Vehicle Ground Speed Estimation in Airborne Videos

2019 ◽  
Vol 11 (10) ◽  
pp. 1241 ◽  
Author(s):  
Jing Li ◽  
Shuo Chen ◽  
Fangbing Zhang ◽  
Erkang Li ◽  
Tao Yang ◽  
...  
Keyword(s):  
Rapid Development ◽  
Detection Algorithm ◽  
Surveillance Systems ◽  
Speed Estimation ◽  
Vehicle Speed ◽  
Aerial Surveillance ◽  
Current Frame ◽  
Ground Speed ◽  
Optical Flow Method ◽  
Adaptive Framework

With the rapid development of unmanned aerial vehicles (UAVs), UAV-based intelligent airborne surveillance systems represented by real-time ground vehicle speed estimation have attracted wide attention from researchers. However, there are still many challenges in extracting speed information from UAV videos, including the dynamic moving background, small target size, complicated environment, and diverse scenes. In this paper, we propose a novel adaptive framework for multi-vehicle ground speed estimation in airborne videos. Firstly, we build a traffic dataset based on UAV. Then, we use the deep learning detection algorithm to detect the vehicle in the UAV field of view and obtain the trajectory in the image through the tracking-by-detection algorithm. Thereafter, we present a motion compensation method based on homography. This method obtains matching feature points by an optical flow method and eliminates the influence of the detected target to accurately calculate the homography matrix to determine the real motion trajectory in the current frame. Finally, vehicle speed is estimated based on the mapping relationship between the pixel distance and the actual distance. The method regards the actual size of the car as prior information and adaptively recovers the pixel scale by estimating the vehicle size in the image; it then calculates the vehicle speed. In order to evaluate the performance of the proposed system, we carry out a large number of experiments on the AirSim Simulation platform as well as real UAV aerial surveillance experiments. Through quantitative and qualitative analysis of the simulation results and real experiments, we verify that the proposed system has a unique ability to detect, track, and estimate the speed of ground vehicles simultaneously even with a single downward-looking camera. Additionally, the system can obtain effective and accurate speed estimation results, even in various complex scenes.

scholarly journals Deep Learning-Based Congestion Detection at Urban Intersections

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2052
Author(s):  
Xinghai Yang ◽  
Fengjiao Wang ◽  
Zhiquan Bai ◽  
Feifei Xun ◽  
Yulin Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optical Flow ◽  
Traffic Congestion ◽  
Detection Algorithm ◽  
Input Image ◽  
Vehicle Speed ◽  
Position Information ◽  
Traffic State ◽  
State Discrimination ◽  
Discrimination Method

In this paper, a deep learning-based traffic state discrimination method is proposed to detect traffic congestion at urban intersections. The detection algorithm includes two parts, global speed detection and a traffic state discrimination algorithm. Firstly, the region of interest (ROI) is selected as the road intersection from the input image of the You Only Look Once (YOLO) v3 object detection algorithm for vehicle target detection. The Lucas-Kanade (LK) optical flow method is employed to calculate the vehicle speed. Then, the corresponding intersection state can be obtained based on the vehicle speed and the discrimination algorithm. The detection of the vehicle takes the position information obtained by YOLOv3 as the input of the LK optical flow algorithm and forms an optical flow vector to complete the vehicle speed detection. Experimental results show that the detection algorithm can detect the vehicle speed and traffic state discrimination method can judge the traffic state accurately, which has a strong anti-interference ability and meets the practical application requirements.

The Analysis and Design of the Dynamic Information Service System Network Architecture of the SOA Based on WAVE Technology

2014 ◽  
Vol 1044-1045 ◽  
pp. 1380-1383
Author(s):  
Guang Li Yin
Keyword(s):  
Network Architecture ◽  
High Speed ◽  
Service System ◽  
Road Traffic ◽  
Rapid Development ◽  
Information Service ◽  
Advanced Technology ◽  
Living Standard ◽  
Vehicle Speed ◽  
Analysis And Design

Safety problem is one of the most attention and concern of driving. This paper in the high-speed on the road cars and car, car and road communications, vehicle real-time status, through the network information service system integration on a platform, on the use of related technologies are analyzed, the design of the software system based on SOA architecture.Keywords: network, GPS module, SOA cross platformI. IntorductionWith the development of science and technology and the improvement of people's living standard, Car popularity rate is high, it's hard to believe, families has two or three car. Whether it is the bus or private car is such rapid development, this will bring a lot of problems in road traffic, such as traffic congestion, traffic accident. These problems affect the normal life and travel, it is necessary to carry out management and provide information service for road use advanced technology. Using mobile phone GPS positioning module can obtain the vehicle speed and the basic information, through processing and optimization of information service system, the analysis of data useful, so as to divert traffic, both for the convenience of the user, but also improve the expressway management ability.

Acoustic vehicle speed estimation from single sensor measurements

2021 ◽  
pp. 1-1
Author(s):  
Slobodan Djukanovic ◽  
Jiri Matas ◽  
Tuomas Virtanen
Keyword(s):  
Speed Estimation ◽  
Vehicle Speed ◽  
Single Sensor

scholarly journals IoT Driven Automated Object Detection Algorithm for Urban Surveillance System in Smart City

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1687-1691
Keyword(s):  
Object Detection ◽  
Road Traffic ◽  
Smart Cities ◽  
Digital Camera ◽  
Detection Algorithm ◽  
License Plate ◽  
Surveillance Systems ◽  
Research Issues ◽  
Automated Object Detection ◽  
Research Challenge

Automated object detection algorithm is an important research challenge in intelligent urban surveillance systems for Internet of Things (IoT) and smart cities applications. In particular, smart vehicle license plate recognition and vehicle detection are recognized as core research issues of these IoTdriven intelligent urban surveillance systems. They are key techniques in most of the traffic related IoT applications, such as road traffic real-time monitoring, security control of restricted areas, automatic parking access control, searching stolen vehicles, etc. In this paper, we propose a novel unified method of automated object detection for urban surveillance systems. We use this novel method to determine and pick out the highest energy frequency areas of the images from the digital camera imaging sensors, that is, either to pick the vehicle license plates or the vehicles out from the images. The other sensors like flame and ultrasonic sensor are used to monitor nearby objects. Our proposed method can not only help to detect object vehicles rapidly and accurately, but also can be used to reduce big data volume needed to be stored in urban surveillance systems

scholarly journals Cyber-Physical Attack Detection and Recovery based on RNN in Automotive Brake Systems

2018 ◽  
Author(s):  
Jongho Shin ◽  
Youngmi Baek ◽  
Jaeseong Lee ◽  
Seonghun Lee
Keyword(s):  
Classification Accuracy ◽  
Weighted Average ◽  
Data Integrity ◽  
Wheel Speed ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Speed Estimation ◽  
Vehicle Speed ◽  
Detection And Identification ◽  
Fault Detection And Identification

The violation of data integrity in automotive Cyber-Physical Systems (CPS) may lead to dangerous situations for drivers and pedestrians in terms of safety. In particular, cyber-attacks on the sensor could easily degrade data accuracy and consistency over any other attack, we investigate attack detection and identification based on a deep learning technology on wheel speed sensors of automotive CPS. For faster recovery of a physical system with detection of the cyber-attacks, estimation of a specific value is conducted to substitute false data. To the best of our knowledge, there has not been a case of joining sensor attack detection and vehicle speed estimation in existing literatures. In this work, we design a novel method to combine attack detection and identification, vehicle speed estimation of wheel speed sensors to improve the safety of CPS even under the attacks. First, we define states of the sensors based on the cases of attacks that can occur in the sensors. Second, Recurrent Neural Network (RNN) is applied to detect and identify wheel speed sensor attacks. Third, in order to estimate the vehicle speeds accurately, we employ Weighted Average (WA), as one of the fusion algorithms, in order to assign a different weight to each sensor. Since environment uncertainty while driving has an impact on different characteristics of vehicles and cause performance degradation, the recovery mechanism needs the ability adaptive to changing environments. Therefore, we estimate the vehicle speeds after assigning a different weight to each sensor depending on driving situations classified by analyzing driving data. Experiments including training, validation, and test are carried out with actual measurements obtained while driving on the real road. In case of the fault detection and identification, classification accuracy is evaluated. Mean Squared Error (MSE) is calculated to verify that the speed is estimated accurately. The classification accuracy about test additive attack data is 99.4978%. MSE of our proposed speed estimation algorithm is 1.7786. It is about 0.2 lower than MSEs of other algorithms. We demonstrate that our system maintains data integrity well and is safe relatively in comparison with systems which apply other algorithms.

CARRIED BAGGAGE DETECTION AND CLASSIFICATION USING MULTI-TREND BINARY CODE DESCRIPTOR AND SUPPORT VECTOR MACHINE

2021 ◽  
Author(s):  
Shah bano ◽  
Syed Adnan Shah ◽  
Wakeel Ahmad ◽  
Muhammad Ilyas
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Binary Code ◽  
Spatial Information ◽  
Detection Algorithm ◽  
Support Vector ◽  
Surveillance Systems ◽  
Body Parts ◽  
Public Places ◽  
Surveillance Camera

Automatic video surveillance systems have gained significant importance due to an increase in crime rate over the last two decades. Automatic baggage detection through surveillance camera can help in security and monitoring in public places. A detection algorithm for humans (with or without carrying baggage) is proposed in this paper. Detection in the proposed method can be achieved by employing spatial information of the baggage of various texture patterns with locus to the human body carrying it. To extract the features of body parts (such as head, trunk and limbs), the descriptor is exhibited and trained by the support vector machine classifier. The proposed approach has been widely assessed by using publically available datasets. The experimental results have shown that the proposed approach is viable for baggage detection and classification as compared to the other available approaches.

Sign in / Sign up

Export Citation Format

Share Document