Moving Vehicle Detection Research Articles

Damage Identification Based on the Loaded Frequency Changes and Deep Learning for Bridge

Frequency is widely used in damage identification as the dynamic characteristic parameter with the simplest measurement method and the highest measurement accuracy, but the frequency changes are nonunique parameters of damage location for the symmetrical structure. To improve the method of damage location identification based on frequency changes for bridge, the bridge damage identification network based on loaded frequency changes and deep learning theory is proposed by introducing the moving vehicle detection method. A continuous beam example is used for testing, and the test results show that the identification network based on the bridge load frequency changes and the Stacked Denoising Auto-Encoder(SDAE) network can effectively locate the damage of bridge, which enables the frequency changes data to overcome the nonunique limitation of the symmetrical structure, enhances the expression ability of the parameters and improves the effectiveness and the anti-noise property of bridge damage location identification.

Cross-frame keypoint-based and spatial motion information-guided networks for moving vehicle detection and tracking in satellite videos

Deep learning methods have achieved the state-of-the-art performance of object detection and tracking in natural images, such as keypoint-based detectors and appearance/motion-based trackers. However, for small and blurry moving vehicles in satellite videos, keypoint-based detectors cause the missed detection of keypoints and incorrect keypoint matching. In terms of multi-object tracking, it is difficult to track the crowded similar vehicles stably only by using the appearance or motion information. To address these problems, a novel deep learning framework is proposed for moving vehicle detection and tracking in the satellite videos. It is comprised of the cross-frame keypoint-based detection network (CKDNet) and spatial motion information-guided tracking network (SMTNet). In CKDNet, a customized cross-frame module is designed to assist the detection of keypoints by exploiting complementary information between frames. Furthermore, CKDNet improves keypoint matching by incorporating size prediction around the keypoints and defining the soft mismatch suppression for out-of-size keypoint pairs. Based on high-quality detection, SMTNet can track the densely-packed vehicles effectively by constructing two-branch long short-term memories. It extracts not only spatial information of the same frame by considering the relative spatial relationship of neighboring vehicles, but also motion information among consecutive frames by calculating the movement velocity. Especially, it regresses virtual positions for missed or occluded vehicles and keeps on tracking these vehicles while they reappear. Experimental results on Jilin-1 and SkySat satellite videos demonstrate the effectiveness of the proposed detection and tracking methods.

RFID Based Vehicle Toll Collection System for Toll Roads

The RFID-based vehicle collection program is intended to better handle toll operations through technology that aims to streamline the flow of vehicles. The purpose of this work is to plan, introduce and promote the automated operation of the car selection system (VTS). The Vehicle Toll Collection Device in this paper automatically detects vehicles and gathers machine-readable details on tolls for automobiles driving in the toll road. This knowledge is instigated by the modification and installation of at least one vehicle with a moving vehicle detection device. The computerized control device located along the toll line will transmit the registration signal as the car is reaching the registration point and will determine the toll to be debited and transfer the toll electronically to the account of the individual vehicle. This device helps a car to proceed beyond the scan point with no halting, thereby providing commuters with optimum comfort, speeding up traffic movement and reducing the need for human capital on highways

V-DaT: A Robust method for Vehicle Detection and Tracking

Vision-based traffic surveillance has been one of the most promising fields for improvement and research. Still, many challenging problems remain unsolved, such as addressing vehicle occlusions and reducing false detection. In this work, a method for vehicle detection and tracking is proposed. The proposed model considers background subtraction concept for moving vehicle detection but unlike conventional approaches, here numerous algorithmic optimization approaches have been applied such as multi-directional filtering and fusion based background subtraction, thresholding, directional filtering and morphological operations for moving vehicle detection. In addition, blob analysis and adaptive bounding box is used for Detection and Tracking. The Performance of Proposed work is measured on Standard Dataset and results are encouraging.

Tiny moving vehicle detection in satellite video with constraints of multiple prior information

ABSTRACT Ｗith the rapid development of remote sensing, satellite video has become an important data source for vehicle detection, which provides a broader field of surveillance. The achieved work generally focuses on aerial video with moderately sized objects based on feature extraction. However, the moving vehicles in satellite video imagery range from just a few pixels to dozens of pixels and exhibit low contrast with respect to the background, which makes it hard to get available appearance or shape information this paper, a tiny vehicle detection method based on spatio-temporal information is proposed to constrain the significance of the image. Firstly, the background modelling method is used to obtain the motion heat map of the image and constrain the motion region. A significance detection method for small targets was used to obtain the significance mapping of these regions. Finally, the detection results were optimized by combining the significance neighbourhood information and the time information between frames to output the binary target detection map. Finally, taking different urban road scenes in ‘Jilin-1’satellite video as examples and compares a variety of existing algorithms. Experiments prove that the proposed algorithm can maintain false alarm rate of less than 10% when the detection accuracy and recall rate reach 85% and has certain anti-interference ability in the image environment with satellite Angle deviation.

An Analysis on Scheduling of Traffic Light at Urban Traffic Intersection Using Fuzzy Control Algorithm

In modern society, private cars have become the first choice for many families because of their convenience and versatility. The volume of vehicles on the road is the basis of traffic accident and traffic congestion. In urban sector the traffic congestion is normally high due to the green light time interval at four road intersections. The traffic light control and time setting are basically timer control operation at current traffic control management system, this shows that, the current system is not intelligent so that there is still heavy traffic congestion. It is vital to implement routinely adjusted schedule as per the real-time position of vehicles at urban cross road intersection. Now, there are various detector systems for traffic monitoring, like Inductive Loop microwave radar, laser, infrared, ultrasonic, magnetometer and video image processing. But they have relevant weakness, such as high cost and complex technology. As a more and more widely used technology, image processing plays an important role in the management and control of intelligent transportation system. Image processing systems are based on motion detection of vehicles, wherein computer vision algorithms extract vehicles from traffic video data for traffic density estimations. This paper is an analysis on scheduling of traffic light of traffic management system using Fuzzy Control Algorithm. With the increase of the number of vehicles and population, it will also improve the traffic jam and the mood of people because of the cause of jam. Rather than previous technology, it will be low cost and simple, which can be adopted in every place as far as possible. MATLAB tool was used to figure out the variables impact on scheduling of traffic light at urban traffic intersection. The vehicle number, vehicle speed, lane length and vehicle type variables are identified and tested against vehicle driving for conclusion on traffic management performance. From findings the results were identified as the vehicle number, vehicle speed, and vehicle type have significant positive relationship with vehicle driving. However, the lane length did not significantly affect the vehicle driving. This indicates that the lane length is less important in scheduling of traffic light at urban traffic intersection.

Moving vehicle detection from video sequences for Traffic Surveillance System

In the current scenario, Intelligent Transportation Systems play a significant role in smart city platform. Automatic moving vehicle detection from video sequences is the core component of the automated traffic management system. Humans can easily detect and recognize objects from complex scenes in a flash. Translating that thought process to a machine, however, requires us to learn the art of object detection using computer vision algorithms. This paper solves the traffic issues of the urban areas with an intelligent automatic transportation system. This paper includes automatic vehicle counting with the help of blob analysis, background subtraction with the use of a dynamic autoregressive moving average model, identify the moving objects with the help of a Boundary block detection algorithm, and tracking the vehicle. This paper analyses the procedure of a video-based traffic congestion system and divides it into greying, binarisation, de-nosing, and moving target detection. The investigational results show that the planned system can provide useful information for traffic surveillance.

Moving Vehicle Detection and Classification Using Gaussian Mixture Model and Ensemble Deep Learning Technique

In recent decades, automatic vehicle classification plays a vital role in intelligent transportation systems and visual traffic surveillance systems. Especially in countries that imposed a lockdown (mobility restrictions help reduce the spread of COVID‐19), it becomes important to curtail the movement of vehicles as much as possible. For an effective visual traffic surveillance system, it is essential to detect vehicles from the images and classify the vehicles into different types (e.g., bus, car, and pickup truck). Most of the existing research studies focused only on maximizing the percentage of predictions, which have poor real‐time performance and consume more computing resources. To highlight the problems of classifying imbalanced data, a new technique is proposed in this research article for vehicle type classification. Initially, the data are collected from the Beijing Institute of Technology Vehicle Dataset and the MIOvision Traffic Camera Dataset. In addition, adaptive histogram equalization and the Gaussian mixture model are implemented for enhancing the quality of collected vehicle images and to detect vehicles from the denoised images. Then, the Steerable Pyramid Transform and the Weber Local Descriptor are employed to extract the feature vectors from the detected vehicles. Finally, the extracted features are given as the input to an ensemble deep learning technique for vehicle classification. In the simulation phase, the proposed ensemble deep learning technique obtained 99.13% and 99.28% of classification accuracy on the MIOvision Traffic Camera Dataset and the Beijing Institute of Technology Vehicle Dataset. The obtained results are effective compared to the standard existing benchmark techniques on both datasets.

Moving Vehicle Detection for Remote Sensing Video Surveillance With Nonstationary Satellite Platform.

With satellite platforms gazing at a target territory, the captured satellite videos exhibit local misalignment and local intensity variation on some stationary objects that can be mistakenly extracted as moving objects and increase false alarm rates. Typical approaches for mitigating the effect of moving cameras in moving object detection (MOD) follow domain transformation technique, where the misalignment between consecutive frames is restricted to the image planar. However, such technique cannot properly handle satellite videos, as the local misalignment on them is caused by the varying projections from the 3D objects on the Earth's surface to 2D image planar. In order to suppress the effect of moving satellite platform in MOD, we propose a Moving-Confidence-Assisted Matrix Decomposition (MCMD) model, where foreground regularization is designed to promote real moving objects and ignore system movements with the assistance of a moving-confidence score estimated from dense optical flows. For solving the convex optimization problem in MCMD, both batch processing and online solutions are developed in this study, by adopting the alternating direction method and the stochastic optimization strategy, respectively. Experimental results on the videos captured by SkySat and Jilin-1 show that MCMD outperforms the state-of-the-art techniques with improved precision by suppressing effect of nonstationary satellite platforms.

A Novel AMS-DAT Algorithm for Moving Vehicle Detection in a Satellite Video

Satellite videos have recently served as a new data source for a wide range of applications in traffic management and military surveillance. Due to its wider coverage, satellite videos show more advantages in large-scale monitoring than ground surveillance videos. However, pseudomotion background and low-resolution targets pose new challenges to moving vehicle detection in satellite videos, resulting in poor performance of conventional target detection methods when applied to satellite videos. To overcome this difficulty, we propose a novel moving vehicle detection approach using adaptive motion separation and difference accumulated trajectory. Specifically, a new indicator is designed to assist adaptive separation of moving targets and background, considering the scale invariance of vehicles in satellite videos. Meanwhile, we offer a vehicle discrimination algorithm based on a differential accumulated trajectory to distinguish the moving vehicles from the pseudomotion background. Experimental results on two satellite video data sets demonstrate that the proposed approach achieves better detection performance over the state-of-the-art moving vehicle detection methods.

Moving Vehicle Detection Based on RPCA Using Multisquint Spaceborne SAR Images

In this letter, a moving vehicle detection method is proposed based on spaceborne synthetic aperture radar images, generated by antenna azimuth steering with different azimuth angles and time lags. First, the multisquint geometry and sequential image model are established, and the variation from stationary scatters over sequence is addressed, which interferes the indication of moving signals significantly. Then, the robust principal component analysis (RPCA) is employed in the image domain as the predetection step. Next, with some spatial filters and thresholding estimators, the moving target signature is exploited to further discriminate the residual false detection. Finally, the proposed method is verified by both simulated and real images based on TerraSAR-X data. Results reveal that the clutter suppression can be effectively achieved.

Research on Video-based Moving Vehicle Detection Technology

Moving vehicle detection technology has been used more and more widely in monitoring traffic conditions such as vehicle flow, vehicle speed, traffic accidents, and escape from violations. The most commonly used methods of moving vehicle detection technology include the inter-frame difference method, the background difference method, and the optical flow method. The inter-frame difference method subtracts the images of two or three adjacent frames, the pixel whose brightness change does not exceed the threshold value is considered as the background, and the pixel whose brightness change exceeds the threshold value is considered as the foreground; The background difference method uses a difference comparison between the current image and the background model to realize the moving vehicle detection; The optical flow method is to construct an optical flow field by changing the brightness value of each pixel point in an adjacent frame in a video image, and realize the detection of a moving vehicle through a moving field of the moving vehicle. The frame difference method can detect the contour edge information of a moving vehicle, and the detection result is less noisy; the background difference method can detect a more complete moving vehicle, including not only the contour information of the moving vehicle, but also the connected area inside the moving vehicle; The optical flow method can detect moving vehicles, but can only obtain the contour edge information of moving vehicles, and there are large voids in the interior of moving vehicles. In short, these methods can realize the detection of moving vehicles, but there are still some problems. With the research and implementation of improved algorithms, the detection technology of moving vehicles will be more and more widely used.

Three-Dimensional Device-Free Localization for Vehicle.

Device-free localization (DFL) is a promising technique which could provide localization information for a target without requiring an electronic device. With the development of the smart city and smart transportation, DFL could form part of a basic technique that could be used to track and localize roadside vehicles. In this paper, some algorithms for three-dimensional (3D) DFL for vehicle surveillance are developed, including statistical methods for data, a method for communication link selection, a novel method of communication link weight allocation and some other minor approaches to obtain the location and approximate size of a static vehicle, as a basic technique of moving vehicle detection. Then, an experimental system is designed. Through security monitoring wireless sensor networks (WSN), real-time vehicle characteristics (i.e., location and size) are calculated automatically and intuitively displayed through a heat map. Experiments are performed to validate the effect of the proposal and the accuracy of the localization and size estimation.

Ground Moving Vehicle Detection and Movement Tracking Based on the Neuromorphic Vision Sensor

Moving-objects detection is a critical ability for an autonomous vehicle. Facing the high detection requirements and the slow target-extraction problem of a common camera, this article proposes to utilize neuromorphic vision sensor (DVS) for detecting the moving objects and estimating their movement states. For a better detection work, the DVS image’s noise points are filtered and the CTRV kinematics model is built in advance. In order to distinguish the overlapped or nearby bodies and get the accurate clustering number, this article proposes a 3-D improved $K$ -means method. As the clustering centers can be influenced by the movement easily, the moving objects’ clustering centers appear unstable, so the movement estimation also fluctuates. In order to obtain stable movement estimation, this article proposes a strong tracking center differential external Kalman filter (SCDEKF) to track the moving objects, and the method has higher accuracy and less computational load. In order to verify the advantages of proposed methods, a simulation environment was established in the Gazebo, and the common cameras were also added in simulation for comparison with the DVS sensor. The simulation results show that the 3-D improved $K$ -means method with DVS can cluster the moving objects accurately, and the SCDEKF can provide more accurate movement estimation than the traditional EKF method. Finally, two experiments were conducted to prove the methods’ superiority. The main contribution is to solve the exploitation problems faced by the short-term borne sensor and promote its application in transportation.

Moving Vehicle Detection and Tracking Based on Video Sequences

Moving Vehicle Detection and Tracking Based on Video Sequences

Moving Vehicle Detection and Tracking Based on Optical Flow Method and Immune Particle Filter under Complex Transportation Environments

Video-based moving vehicle detection and tracking is an important prerequisite for vehicle counting under complex transportation environments. However, in the complex natural scene, the conventional optical flow method cannot accurately detect the boundary of the moving vehicle due to the generation of the shadow. In addition, traditional vehicle tracking algorithms are often occluded by trees, buildings, etc., and particle filters are also susceptible to particle degradation. To solve this problem, this paper proposes a kind of moving vehicle detection and tracking based on the optical flow method and immune particle filter algorithm. The proposed method firstly uses the optical flow method to roughly detect the moving vehicle and then uses the shadow detection algorithm based on the HSV color space to mark the shadow position after threshold segmentation and further combines the region-labeling algorithm to realize the shadow removal and accurately detect the moving vehicle. Improved affinity calculation and mutation function of antibody are proposed to make the particle filter algorithm have certain adaptivity and robustness to scene interference. Experiments are carried out in complex traffic scenes with shadow and occlusion interference. The experimental results show that the proposed algorithm can well solve the interference of shadow and occlusion and realize accurate detection and robust tracking of moving vehicles under complex transportation environments, which has the potentiality to be processed on a cloud computing platform.

Moving Vehicle Detection and Drone Velocity Estimation with a Moving Drone

Moving Vehicle Detection and Drone Velocity Estimation with a Moving Drone

Optimal feed forward neural network based automatic moving vehicle detection system in traffic surveillance system

In intelligent transportation system, traffic surveillance is an important topic. One challenging problem for complex urban traffic surveillance system is robust vehicle detection and tracking. Therefore, in this paper, we develop a two-stage approach for moving vehicle detection system. The proposed system mainly consists of two stages such as hypothesis generation (HG) and hypothesis verification (HV). In the first step, we generate the hypotheses using shadows under vehicles is darker than road region concept. In the second step, we verify hypotheses generated in the first step whether correct or not using optimal feedforward neural network (OFFNN). Here, to extract vehicle features, we utilize two types of histogram orientation gradients descriptors (HOG). In training stage, the histogram orientation gradients features are given to the OFFNN classifier. The weights corresponding FFNN is optimally select using improved grasshopper optimization algorithm (IGOA). The experimental results show that the proposed moving vehicle detection system performs better accuracy compare to other methods.

Moving vehicle detection in aerial infrared image sequences via fast image registration and improved YOLOv3 network

ABSTRACTOwing to its powerful adaptability and robustness to the weak light, the infrared camera equipped on the unmanned aerial vehicle is more and more applied for aerial photography. Nowadays, how to make full use of the aerial infrared image sequences for the moving vehicle detection has attracted widespread attention. However, due to the low-resolution, low contrast, and few texture features of the infrared image, it is extremely difficult to detect the moving vehicles. In our work, an effective and efficient moving vehicle detection approach in the aerial infrared image sequences is proposed via fast image registration and You Only Look Once Version 3 (YOLOv3) network. First, to compensate for the motion of the aerial infrared camera, a fast infrared image registration method is put forward. To improve the accuracy and efficiency of image registration, we construct a multi-screening based mechanism for screening out the incorrect and redundant feature points. For feature description, the low-level and high-level descriptors are combined to further improve the registration accuracy. Then, the 2-frame difference and image masking are introduced to acquire the frame mask images, where only the region-of-interest is reserved, and the remaining regions are masked. Next, we construct a new structure of an improved YOLOv3 network with only 23 layers. Due to the insufficiency of the infrared vehicle samples, transfer learning is introduced to train the improved YOLOv3 network. Finally, the proposed approach is evaluated on the Defence Advanced Research Projects Agency (DARPA) Video verification of Identify (VIVID) and Northwest Polytechnical University (NPU) data sets. Experiments and comprehensive analyses show that the proposed approach can achieve satisfactory and competitive moving vehicle detection results.

Moving vehicle detection for remote sensing satellite video

Moving vehicle detection for remote sensing satellite video