scholarly journals Moving Vehicle Tracking with a Moving Drone Based on Track Association

2021 ◽  
Vol 11 (9) ◽  
pp. 4046
Author(s):  
Seokwon Yeom ◽  
Don-Ho Nam
Keyword(s):  
Estimation Error ◽  
Detection Rates ◽  
Detection Scheme ◽  
Moving Vehicle ◽  
Track Fusion ◽  
Single Target ◽  
Moving Vehicles ◽  
Track Association ◽  
Object Area ◽  
Average Position

The drone has played an important role in security and surveillance. However, due to the limited computing power and energy resources, more efficient systems are required for surveillance tasks. In this paper, we address detection and tracking of moving vehicles with a small drone. A moving object detection scheme has been developed based on frame registration and subtraction followed by morphological filtering and false alarm removing. The center position of the detected object area is the input to the tracking target as a measurement. The Kalman filter estimates the position and velocity of the target based on the measurement nearest to the state prediction. We propose a new data association scheme for multiple measurements on a single target. This track association method consists of the hypothesis testing between two tracks and track fusion through track selection and termination. We reduce redundant tracks on the same target and maintain the track with the least estimation error. In the experiment, drones flying at an altitude of 150 m captured two videos in an urban environment. There are a total of 9 and 23 moving vehicles in each video; the detection rates are 92% and 89%, respectively. The number of valid tracks is significantly reduced from 13 to 10 and 56 to 26 in the first and the second video, respectively. In the first video, the average position RMSE of two merged tracks are improved by 83.6% when only the fused states are considered. In the second video, the average position and velocity RMSE are 1.21 m and 1.97 m/s, showing the robustness of the proposed system.

scholarly journals Ground Target Tracking Using an Airborne Angle-Only Sensor with Terrain Uncertainty and Sensor Biases

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 509
Author(s):  
Dipayan Mitra ◽  
Aranee Balachandran ◽  
Ratnasingham Tharmarasa
Keyword(s):  
Lower Bound ◽  
Estimation Error ◽  
Common Source ◽  
Bias Estimation ◽  
Tracking Accuracy ◽  
Digital Terrain ◽  
Single Target ◽  
Elevation Data ◽  
Measurement Biases ◽  
Terrain Elevation

Airborne angle-only sensors can be used to track stationary or mobile ground targets. In order to make the problem observable in 3-dimensions (3-D), the height of the target (i.e., the height of the terrain) from the sea-level is needed to be known. In most of the existing works, the terrain height is assumed to be known accurately. However, the terrain height is usually obtained from Digital Terrain Elevation Data (DTED), which has different resolution levels. Ignoring the terrain height uncertainty in a tracking algorithm will lead to a bias in the estimated states. In addition to the terrain uncertainty, another common source of uncertainty in angle-only sensors is the sensor biases. Both these uncertainties must be handled properly to obtain better tracking accuracy. In this paper, we propose algorithms to estimate the sensor biases with the target(s) of opportunity and algorithms to track targets with terrain and sensor bias uncertainties. Sensor bias uncertainties can be reduced by estimating the biases using the measurements from the target(s) of opportunity with known horizontal positions. This step can be an optional step in an angle-only tracking problem. In this work, we have proposed algorithms to pick optimal targets of opportunity to obtain better bias estimation and algorithms to estimate the biases with the selected target(s) of opportunity. Finally, we provide a filtering framework to track the targets with terrain and bias uncertainties. The Posterior Cramer–Rao Lower Bound (PCRLB), which provides the lower bound on achievable estimation error, is derived for the single target filtering with an angle-only sensor with terrain uncertainty and measurement biases. The effectiveness of the proposed algorithms is verified by Monte Carlo simulations. The simulation results show that sensor biases can be estimated accurately using the target(s) of opportunity and the tracking accuracies of the targets can be improved significantly using the proposed algorithms when the terrain and bias uncertainties are present.

Moving Vehicle Detection Using Time Domain Statistical Features

2013 ◽  
Vol 471 ◽  
pp. 208-212 ◽  
Author(s):  
M.P. Paulraj ◽  
Hamid Adom Abdul ◽  
Marhainis Othman Siti ◽  
Sundararaj Sathishkumar
Keyword(s):  
Probabilistic Neural Network ◽  
Hearing Impaired ◽  
Statistical Features ◽  
Experimental Protocol ◽  
Moving Vehicle ◽  
Vehicle Type ◽  
Moving Vehicles ◽  
Machine Interface ◽  
Moving Vehicle Detection ◽  
Sound Signature

The Hearing Impaired People (HIP) cannot distinguish the sound from a moving vehicle approaching from their behind. Since, it is difficult for hearing impaired to hear and judge sound information and they often encounter risky situations while they are in outdoor. If HIPs can successfully get sound information through some machine interface, dangerous situation will be avoided. Generally the profoundly deaf people do not use any hearing aid which does not provide any benefit. This paper presents, simple statistical features are used to classify the vehicle type and its distance based on sound signature recorded from the moving vehicles. An experimental protocol is designed to record the vehicle sound under different environment conditions and also at different speed of vehicles. Basic statistical features such as the standard deviation, Skewness, Kurtosis and frame energy have been used to extract the features. Probabilistic neural network (PNN) models are developed to classify the vehicle type and its distance. The effectiveness of the network is validated through stimulation.

Moving Vehicles Detection in Traffic Video Using Modified SXCS-LBP Texture Descriptor

2015 ◽  
Vol 5 (2) ◽  
pp. 14-34
Author(s):  
Arun Kumar H. D. ◽  
Prabhakar C. J.
Keyword(s):  
Background Modeling ◽  
Experimental Result ◽  
Texture Descriptor ◽  
Illumination Variation ◽  
Moving Vehicle ◽  
Moving Vehicles ◽  
Spatially Extended ◽  
Traffic Video ◽  
Lbp Descriptor ◽  
Moving Vehicle Detection

Background modeling and subtraction based method for moving vehicle's detection in traffic video using a novel texture descriptor called as Modified Spatially eXtended Center Symmetric Local Binary Pattern (Modified SXCS-LBP) descriptor. The XCS-LBP texture descriptor is sensitive to noise because in order to generate binary code, the value of center pixel value is used as the threshold directly, and it does not consider temporal motion information. In order to solve this problem, this paper proposed a novel texture descriptor called as Modified SXCS-LBP descriptor for moving vehicle detection based on background modeling and subtraction. The proposed descriptor is robust against noise, illumination variation, and able to detect slow moving vehicles because it considers both spatial and temporal moving information. The evaluation carried out using precision and recall metric, which are obtained using experiments conducted on two popular datasets such as BMC and CDnet datasets. The experimental result shows that the authors' method outperforms existing texture and non-texture based methods.

scholarly journals Stopping Accidents before They Happen: Perceiving Lane-Level Moving Vehicle Danger Regions to Warn Surrounding Drivers and Pedestrians

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Chi Guo ◽  
Guangyi Cao ◽  
Jieru Zeng ◽  
Jinsong Cui ◽  
Rong Peng
Keyword(s):  
Real Time ◽  
Large Scale ◽  
The Internet ◽  
Active Safety ◽  
Internet Of Vehicles ◽  
Moving Vehicle ◽  
Moving Vehicles ◽  
Traditional Service ◽  
New Type ◽  
New System

Perceiving the location of dangerous moving vehicles and broadcasting this information to vehicles nearby are essential to achieve active safety in the Internet of Vehicles (IOV). To address this issue, we implement a real-time high-precision lane-level danger region service for moving vehicles. A traditional service depends on static geofencing and fails to deal with dynamic vehicles. To overcome this defect, we devised a new type of IOV service that manages to track dangerous moving vehicles in real time and recognize their danger regions quickly and accurately. Next, we designed algorithms to distinguish the vehicles in danger regions and broadcast the information to these vehicles. Our system can simultaneously manipulate a mass of danger regions for various dangerous vehicles and broadcast this information to surrounding vehicles at a large scale. This new system was tested in Shanghai, Guangzhou, Wuhan, and other cities; the data analysis is presented in this paper as well.

Track association and track fusion with nondeterministic target dynamics

2002 ◽  
Vol 38 (2) ◽  
pp. 659-668 ◽  
Author(s):  
Shozo Mori ◽  
W.H. Barker ◽  
Chee-Yee Chong ◽  
Kuo-Chu Chang
Keyword(s):  
Track Fusion ◽  
Track Association

scholarly journals Moving Load Identification with Long Gauge Fiber Optic Strain Sensing

2021 ◽  
Vol 16 (3) ◽  
pp. 131-158
Author(s):  
Qingqing Zhang ◽  
Wenju Zhao ◽  
Jian Zhang
Keyword(s):  
Fiber Optic ◽  
Moving Load ◽  
Field Testing ◽  
Maximum Strain ◽  
Load Identification ◽  
Moving Vehicle ◽  
Moving Vehicles ◽  
Structural Responses ◽  
Vehicle Loads ◽  
Moving Load Identification

Moving load identification has been researched with regard to the analysis of structural responses, taking into consideration that the structural responses would be affected by the axle parameters, which in its turn would complicate obtaining the values of moving vehicle loads. In this research, a method that identifies the loads of moving vehicles using the modified maximum strain value considering the long-gauge fiber optic strain responses is proposed. The method is based on the assumption that the modified maximum strain value caused only by the axle loads may be easily used to identify the load of moving vehicles by eliminating the influence of these axle parameters from the peak value, which is not limited to a specific type of bridges and can be applied in conditions, where there are multiple moving vehicles on the bridge. Numerical simulations demonstrate that the gross vehicle weights (GVWs) and axle weights are estimated with high accuracy under complex vehicle loads. The effectiveness of the proposed method was verified through field testing of a continuous girder bridge. The identified axle weights and gross vehicle weights are comparable with the static measurements obtained by the static weighing.

scholarly journals Impulsive State Estimation for Nonlinear Systems with Redundant Communication Channels and Markov Delay

2021 ◽  
Author(s):  
Hong-Xia Rao ◽  
Yuru Guo ◽  
Ye Kuang ◽  
Ming Lin ◽  
Yong Xu
Keyword(s):  
Nonlinear Systems ◽  
State Estimation ◽  
Communication Channel ◽  
Impulsive Control ◽  
Estimation Error ◽  
Packet Dropout ◽  
Moving Vehicle ◽  
Current State ◽  
Error System ◽  
Impulsive Control Strategy

Abstract The state estimation issue for the discrete-time nonlinear systems with Markov delay is investigated in this paper, where the redundant communication channel is considered to ensure the reliability of transmission. Because the channel capacity is limited, the packet dropout conditions of the main channel and the redundant channel are described by the Bernoulli stochastic variables. In addition, a mode-dependent estimator is proposed based on the current state and the delayed state, simultaneously. Combining with the impulsive control strategy, the efficiency of estimator is improved. An augmented estimation error system is proposed to deal with the Markov delay in the nonlinear system, subsequently, a sufficient condition that ensures the asymptotic stability of the augmented error system is obtained by a constructed Lyapunov functional candidate and the gains of the impulsive estimator are derived. Finally, a numerical example of moving vehicle is utilized to illustrate the developed results.

scholarly journals Detection of Moving Vehicles on Highway using Fuzzy Logic for Smart Surveillance System

2021 ◽  
Vol 12 (1S) ◽  
pp. 419-431
Author(s):  
Ajay Suria, Et. al.
Keyword(s):  
Fuzzy Logic ◽  
Visible Region ◽  
Illumination Variation ◽  
Moving Vehicle ◽  
Medical Security ◽  
Moving Vehicles ◽  
Smart Surveillance ◽  
Paper Work ◽  
Fast Motion ◽  
Smart Surveillance System

The computer vision incorporates a prominent performance in developing models for medical, security and lot more concepts and we need to deal with detecting and tracking the moving objectsuch as moving vehicle or person. Various challenges are available due to environmental issues, illumination variation, or fast motion etc. This paper work has developed a fuzzy logic based method for identifying the moving vehicles though bounding boxas depicted in the green colour. The paper depictsa fuzzy based method to detect and track the object around the location until the specific secured dimension of the device. The extended recognition (EIR) is the proposed method which works on the automatic fuzzy set creation.The EIR consists of the pixels of the surroundings and recognize them with the predefined inputs we have with the EIR algorithm in the repository. This methodology successfully identified and detects vehicles. It can track the instances within that visible region and this is working as a human eye mechanism.

scholarly journals Tire–Pavement Contact-Aware Weight Estimation for Multi-Sensor WIM Systems

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2027 ◽  
Author(s):  
Zhixin Jia ◽  
Kaiya Fu ◽  
Mengxiang Lin
Keyword(s):  
Neural Network ◽  
Estimation Error ◽  
Backpropagation Neural Network ◽  
Moving Vehicle ◽  
Multiple Sensors ◽  
Public Road ◽  
Weigh In Motion ◽  
Normal Speed ◽  
Gross Vehicle Weight ◽  
Multiple Sensor

Accurately estimating the weight of a moving vehicle at normal speed remains a challenging problem due to the complex vehicle dynamics and vehicle–pavement interaction. The weighing technique based on multiple sensors has proven to be an effective approach to this task. To improve the accuracy of weigh-in-motion (WIM) systems, this paper proposes a neural network-based method integrating identification and predication. A backpropagation neural network for signal classification (BPNN-i) was designed to identify ideal samples acquired by load sensors closest to the tire-pavement contact area. After that, ideal samples were used to predict the gross vehicle weight by using another backpropagation neural network (BPNN-e). The dataset for training and evaluation was collected from a multiple-sensor WIM (MS-WIM) system deployed in a public road. In our experiments, 96.89% of samples in the test set had an estimation error of less than 5%.

Vehicle Classification and Tracking Based on Particle Swarm Optimization and Meanshift

2010 ◽  
Vol 121-122 ◽  
pp. 417-422
Author(s):  
Bo Li ◽  
Zhi Yuan Zeng ◽  
Ji Xiong Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Vehicle Classification ◽  
Shape Information ◽  
Vehicle Class ◽  
Swarm Optimization ◽  
Optimal Position ◽  
Moving Vehicle ◽  
Moving Vehicles

Vehicle classification and tracking is considered as one of the most challenging problems in the field of pattern recognition. In this paper, Particle Swarm Optimization (PSO) based method is exploited to recognize vehicle classes. Vehicle features, such as vehicle size, shape information, contour information are extracted. Each vehicle class is encoded as a centroid with multidimensional feature and PSO is employed to search the optimal position for each class centroid based on fitness function. After vehicle classification, an improved meanshift algorithm is presented for vehicle tracking. The algorithm’s evaluations on video image series, moving vehicle detection, vehicle classification and tracking are respectively conducted. The results show that PSO ensures a promising and stable performances in recognizing these vehicle classes, and the improved meanshift algorithm can achieve accuracy and real-time for tracking moving vehicles.

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