Moving Vehicle Detection Research Articles

Wireless OTFS-Based Integrated Sensing and Communication for Moving Vehicle Detection

Wireless OTFS-Based Integrated Sensing and Communication for Moving Vehicle Detection

Moving vehicle detection and tracking under hazy environment for traffic surveillance system

Moving vehicle detection and tracking under hazy environment for traffic surveillance system

CFDRM: Coarse-to-Fine Dynamic Refinement Model for Weakly Supervised Moving Vehicle Detection in Satellite Videos

CFDRM: Coarse-to-Fine Dynamic Refinement Model for Weakly Supervised Moving Vehicle Detection in Satellite Videos

SDM-Car: A Dataset for Small and Dim Moving Vehicles Detection in Satellite Videos

SDM-Car: A Dataset for Small and Dim Moving Vehicles Detection in Satellite Videos

Real-Time Moving Vehicle Detection in Satellite Video Based on Historical Differential Information and Grouping Features

Real-Time Moving Vehicle Detection in Satellite Video Based on Historical Differential Information and Grouping Features

Moving Target Detection Algorithm for Millimeter Wave Radar Based on Keystone-2DFFT

Millimeter wave radar has the advantage of all-day and all-weather capability for detection, speed measurement. It plays an important role in urban traffic flow monitoring and traffic safety monitoring. The conventional 2-dimensional Fast Fourier Transform (2DFFT) algorithm is performed target detection in the range-Doppler domain. However, the target motion will induce the range walk phenomenon, which leads to a decrease in the target energy and the performance of the target detection and speed measurement. To solve the above problems, this paper proposes a moving vehicle detection algorithm based on Keystone-2DFFT for a traffic scene. Firstly, this paper constructs and analyzes the Frequency Modulated ContinuousWave (FMCW) moving target signal model under traffic monitoring scenario’s radar observation geometry. The traditional 2DFFT moving target detection algorithm is briefly introduced. Then, based on mentioned signal model, an improved moving vehicle detection algorithm based on Keystone-2DFFT transform is proposed. The method first input the echo, then the range walk is removed by keystone transformation. the keystone transformation is achieved via Sinc interpolation. Next is transform data into range-Doppler domain to perform detection and speed estimation. The algorithm is verified by simulation data and real data.

Velocity Analysis on Moving Objects Detection using Multi-Scale Histogram of Oriented Gradient

An autonomous car is a one-of-a-kind specimen in today's technology. It is an automatic system in which most of the duties that humans undertake in the car can be done automatically with minimum human supervision for road safety features. Moving automobile detections, on the other hand, are prone to more mistakes and can result in undesirable situations such as minor car wrecks. Moving vehicle identification is now done using high-speed cameras or LiDAR, for example, whereas self-driving cars are produced with deep learning, which requires much larger datasets. As a result, there may be greater space for improvement in the moving vehicle detection model. This research intends to create another moving car recognition model that uses multi-scale feature-based detection to improve the model's accuracy while also determining the maximum speed at which the model can detect moving objects. The recommended methodology was to create a lab-scale model that can be used as a guide for video and image capture on the lab-scale model, as well as the speed of the toy vehicles captured from the Arduino Uno machine before testing the car recognition model. According to the data, Multi-Scale Histogram of Oriented Gradient can recognize more objects than Histogram of Oriented Gradient with higher object identification accuracies and precision.

VEHICLE DETECTION & TRACKING

Any machine that moves people or goods qualifies as a vehicle. Vehicles include automobiles, bicycles, buses, aircraft, space shuttles, and a wide variety of others. A practical application of machine learning principles is vehicle detection and vehicle type recognition, which is directly applicable for a variety of operations in a traffic surveillance system and contributes to an intelligent traffic surveillance system. The processing of automatic vehicle identification and recognition using datasets of static images will be covered in this study. The surveillance system features moving vehicle detection and recognition, vehicle count, and permission verification with the organisation. Considering that algorithms are crucial to any machine learning programme, it is crucial that we select the optimum model for our project.

Incorporating Deep Background Prior Into Model-Based Method for Unsupervised Moving Vehicle Detection in Satellite Videos

Background reconstruction is a key step of moving object detection in satellite videos. Most existing model-based methods exploit low-rank prior to recover background, which have achieved good performance but suffered degradation under complex and dynamic scenes. In this paper, we introduce a deep background prior into model-based methods for moving vehicle detection in satellite videos. Our deep background prior is obtained by a background reconstruction network, which can learn to reconstruct background from consecutive frames. By applying our deep background prior into model-based methods, a closed-form solution can be obtained via alternating direction method of multipliers (ADMM) and then detection results can be acquired through iterative optimization. More importantly, our background reconstruction network can be trained in an unsupervised way by introducing specifically designed loss, thus relieving the dependence on large-scale labeled dataset. Extensive experimental results demonstrate the efficiency and effectiveness of the proposed method.

An automatic moving vehicle detection system based on hypothesis generation and verification in a traffic surveillance system

An intelligent transportation system has a major topic called traffic surveillance. In a complex urban Traffic Surveillance System (TSS), booming of vehicle detection and tracking is a dilemma. To overcome the problem, a two stage approach for moving vehicle detection system is proposed in this paper. The proposed system mainly consists of two stages namely, Hypothesis Generation (HG) and Hypothesis Verification (HV). At the first step, hypotheses are generated by shadows beneath the vehicles is darker than the road region concept. In the second step a generated hypothesis is verified as correct or not using Optimal Artificial Neural Network (OANN). The weights corresponding ANN is optimally selected using Grasshopper Optimisation Algorithm (GOA). Through experimental results, it is shown that the proposed moving vehicle detection system has better accuracy compared to other methods.

Deep learning-based multiple moving vehicle detection and tracking using a nonlinear fourth-order reaction-diffusion based multi-scale video object analysis

<p style='text-indent:20px;'>A novel automatic vehicle detection and tracking framework is proposed in this research work. First, the moving vehicles are detected in the frames of the video sequence by combining some deep learning and Gaussian Mixture Model-based object detection techniques. Then, the correspondence between the video objects detected in successive frames is determined using a multi-scale analysis of those objects. A scale-space representation is created by applying the numerical approximation algorithm that solves a nonlinear fourth-order reaction-diffusion based model whose mathematical validity is rigorously investigated here. A color image feature extraction is then performed at each scale using SURF and HOG-based features and the feature vectors determined at multiple scales are next concatenated into a final descriptor. A novel instance matching-based vehicle tracking technique using the distances between these feature vectors is then proposed. The results of the performed detection and tracking simulations are finally discussed.</p>

An automatic moving vehicle detection system based on hypothesis generation and verification in a traffic surveillance system

An automatic moving vehicle detection system based on hypothesis generation and verification in a traffic surveillance system

Self-Supervised Moving Vehicle Detection From Audio-Visual Cues

Robust detection of moving vehicles is a critical task for any autonomously operating outdoor robot or self-driving vehicle. Most modern approaches for solving this task rely on training image-based detectors using large-scale vehicle detection datasets such as nuScenes or the Waymo Open Dataset. Providing manual annotations is an expensive and laborious exercise that does not scale well in practice. To tackle this problem, we propose a self-supervised approach that leverages audio-visual cues to detect moving vehicles in videos. Our approach employs contrastive learning for localizing vehicles in images from corresponding pairs of images and recorded audio. In extensive experiments carried out with a real-world dataset, we demonstrate that our approach provides accurate detections of moving vehicles and does not require manual annotations. We furthermore show that our model can be used as a teacher to supervise an audio-only detection model. This student model is invariant to illumination changes and thus effectively bridges the domain gap inherent to models leveraging exclusively vision as the predominant modality.

Vehicle Detection from Aerial Images with Object and Motion Detection

Moving vehicle detection is one of important issues in surveillance and traffic monitoring applications for aerial images. In this study, a vehicle detection method is proposed by combining motion and object detection. A method based on background modeling and subtraction is applied for motion detection, while Faster-RCNN architecture is used for object detection. Motion detection result is enhanced with the proposed superpixel based refinement method. Experimental study shows that performance of motion detection increases about 8\% for $F_1$ metric with the proposed post processing method. Object detection, motion detection and superpixel segmentation methods interact with each other in parallel processes with the proposed software architecture, which significantly increases the working speed of the method. In last step of the proposed method, each vehicle is tracked with the kalman filter. The performance of proposed method is evaluated on the VIVID dataset. The performance evaluation shows that proposed method increases $F_1$ and recall values significantly compared to the motion and object detection methods alone. It also outperforms SCBU and MCD methods which are widely used for performance comparison in motion detection studies in the literature

Enhancing Front-Vehicle Detection in Large Vehicle Fleet Management

Transportation safety has been widely discussed for avoiding forward collisions. The broad concept of remote sensing can be applied to detect the front of vehicles without contact. The traditional Haar features use adjacent rectangular areas for many ordinary vehicle studies to detect the front vehicle images in practice. This paper focused on large vehicles using a front-installed digital video recorder (DVR) with a near-infrared (NIR) camera. The views of large and ordinary vehicles are different; thus, this study used a deep learning method to process progressive improvement in moving vehicle detection. This study proposed a You Only Look Once version 4 (YOLOv4) supplemented with the fence method, called YOLOv4(III), to enhance vehicle detection. This method had high detection accuracy and low false omission rates using the general DVR equipment, and it provided comparison results. There was no need to have a high specification front camera, and the proposed YOLOv4(III) was found to have competitive performance. YOLOv4(III) reduced false detection rates and had a more stable frame per second (FPS) performance than with Haar features. This improved detection method can give an alert for large vehicle drivers to avoid serious collisions, leading to a reduction in the waste of social resources.

Moving vehicle detection and tracking at roundabouts using deep learning with trajectory union

Moving vehicle detection and tracking at roundabouts using deep learning with trajectory union

Moving Vehicle Detection and Recognition Technology based on Artificial Intelligence

The existing target detection and recognition technology has the problem of fuzzy features of moving vehicles, which leads to poor detection effect. A moving car detection and recognition technology based on artificial intelligence is designed. The point operation is adopted to enhance the high frequency information of the image, increase the image contrast, and delineate the video image tracking target. The motion vector similarity is used to predict the moving target area in the next frame of the image. The texture features of the moving car are extracted by artificial intelligence, and the center moment is calculated by the gray histogram distribution curve, the edge feature extraction algorithm is used to set the detection and recognition mode. Experimental results: under complex conditions, this design technology, compared with the other two kinds of moving vehicle detection and recognition technology, detected three more moving vehicles, which proved that the application prospect of the moving vehicle detection and recognition technology integrated with artificial intelligence is broader.

Very Low-Resolution Moving Vehicle Detection in Satellite Videos

This paper proposes a practical end-to-end neural network framework to detect tiny moving vehicles in satellite videos with low imaging quality. Some instability factors such as illumination changes, motion blurs, and low contrast to the cluttered background make it difficult to distinguish true objects from noise and other point-shaped distractors. Moving vehicle detection in satellite videos can be carried out based on background subtraction or frame differencing. However, these methods are prone to produce lots of false alarms and miss many positive targets. Appearance-based detection can be an alternative but is not well-suited since classifier models are of weak discriminative power for the vehicles in top view at such low resolution. This article addresses these issues by integrating motion information from adjacent frames to facilitate the extraction of semantic features and incorporating the Transformer to refine the features for key points estimation and scale prediction. Our proposed model can well identify the actual moving targets and suppress interference from stationary targets or background. The experiments and evaluations using satellite videos show that the proposed approach can accurately locate the targets under weak feature attributes and improve the detection performance in complex scenarios.

Improving Moving Vehicles Detection and Vehicles Trajectory Plotting Using Optimized Background Subtraction Method

Improving Moving Vehicles Detection and Vehicles Trajectory Plotting Using Optimized Background Subtraction Method

Adaptability Attribute for Smart City: Advanced Adaptive Street Lighting Systems

“Smart City Advanced Adaptive Street Lighting Systems” that aimed to build all hardware/software components of an adaptive urban smart lighting architecture that allows municipalities to manage and control public street lighting lamps. To save energy, the system can automatically modify the brightness of street lamps based on the presence of vehicles (buses/trucks, cars, motorbikes, and bikes) and/or pedestrians in specified locations or segments of the streets/roads of interest. The fundamental contribution of this research is to build a low-cost smart lighting system while also defining an IoT infrastructure in which each lighting pole is a component of a network that can improve the amplitude of the system. In a broader sense, the suggested smart infrastructure can be seen as the foundation for a larger technology architecture aiming at providing value-added services for sustainable cities. The smart architecture consists of a number of subsystems (local controllers, motion sensors, video cameras, and weather sensors) and electronic devices, each of which is responsible for performing certain tasks: Video processing for vehicle motion detection and classification, remote street segment lamp management, single street lamp brightness control, wireless and wired data exchanges, power consumptions analysis and traffic evaluation.