Vehicle Color Research Articles

The effects of vehicle color and travel direction on perceived speed error varies by judgment type among older pedestrians

Objective Misjudgments of vehicle speed or distance frequently lead to collisions, particularly among older pedestrians who are less accurate in estimating vehicle speeds than younger individuals. However, comprehensive studies that assess multiple factors influencing speed perception in older pedestrians are lacking. Methods This research utilized computer simulations to explore how vehicle color (red, green, blue) and direction of travel (approaching or receding) affect perceived speed errors in both relative and absolute judgment scenarios among older pedestrians. Results Data from 38 older adults and 40 college students indicated that red vehicles were associated with fewer perceived speed errors than either green or blue vehicles. Errors increased for vehicles moving away, with absolute judgments showing greater discrepancies than relative ones. Analysis revealed that, across various combinations of the three independent variables—vehicle color, vehicle direction, and judgment type—the older participants exhibited significantly larger perceived speed errors compared to college students. Furthermore, the study identified significant interactions between vehicle color and direction, and between judgment type and vehicle direction. Conclusion Our findings are beneficial in understanding the factors influencing older pedestrians’ speed perceptions, aiding public safety and informing car design to ensure safer roads for older pedestrians.

AutoID GateKnight: Empowering Gate Security with License Plate Recognition and Vehicle Colour Detection

Abstract: AutoID gateKnight represents a significant breakthrough in gate security technology. Its utilization of License Plate Recognition and vehicle color detection technologies not only strengthens access control but also offers a level of adaptability and customization that is unparalleled. At the entrance gate, the system will be able to identify licence plates from passing cars and determine whether or not to allow the vehicle access. The gate will operate automatically, with no human interaction. The device is made up of a video camera that records video frames, which contain a picture of the licence plate and the colour of the car. In order to build the suggested system, colour detection using a colour histogram, easyocr to extract the number plate from the image, and Haar cascade for number plate detection have all been used. We process the number plate and colour of the car after properly identifying it, and then we compare it to the vehicle's authorization and registration status. By compiling both the number plate and color of the vehicle, this system provides a robust and highly accurate result. Additionally, the system maintains an updated database of the cars that have left and entered into the required places within a particular duration.

GAN-Based Data Augmentation with Vehicle Color Changes to Train a Vehicle Detection CNN

Object detection is a challenging task that requires a lot of labeled data to train convolutional neural networks (CNNs) that can achieve human-level accuracy. However, such data are not easy to obtain, as they involve significant manual work and costs to annotate the objects in images. Researchers have used traditional data augmentation techniques to increase the amount of training data available to them. A recent trend in object detection is to use generative models to automatically create annotated data that can enrich a training set and improve the performance of the target model. This paper presents a method of training the proposed ColorGAN network, which is used to generate augmented data for the target domain of interest with the least compromise in quality. We demonstrate a method to train a GAN with images of vehicles in different colors. Then, we demonstrate that our ColorGAN can change the color of vehicles of any given vehicle dataset to a set of specified colors, which can serve as an augmented training dataset. Our experimental results show that the augmented dataset generated by the proposed method helps enhance the detection performance of a CNN for applications where the original training data are limited. Our experiments also show that the model can achieve a higher mAP of 76% when the model is trained with augmented images along with the original training dataset.

Deep Learning-Based Vehicle Type and Color Classification to Support Safe Autonomous Driving

This technology can prevent accidents involving large vehicles, such as trucks or buses, by selecting an optimal driving lane for safe autonomous driving. This paper proposes a method for detecting forward-driving vehicles within road images obtained from a vehicle’s DashCam. The proposed method also classifies the types and colors of the detected vehicles. The proposed method uses a YOLO deep learning network for vehicle detection based on a pre-trained ResNet-50 convolutional neural network. Additionally, a Resnet-50 CNN-based object classifier, using transfer learning, was used to classify vehicle types and colors. Vehicle types were classified into four categories based on size whereas vehicle colors were classified into eight categories. During autonomous driving, vehicle types are used to determine driving lanes, whereas vehicle colors are used to distinguish the road infrastructure, such as lanes, vehicles, roads, backgrounds, and buildings. The datasets used for learning consisted of road images acquired in various driving environments. The proposed method achieved a vehicle detection accuracy of 91.5%, vehicle type classification accuracy of 93.9%, and vehicle color classification accuracy of 94.2%. It accurately detected vehicles and classified their types and colors. These can be applied to autonomous and safe driving support systems to enhance the safety of autonomous vehicles.

Multi-camera trajectory matching based on hierarchical clustering and constraints

AbstractThe fast improvement of deep learning methods resulted in breakthroughs in image classification, object detection, and object tracking. Autonomous driving and traffic monitoring systems, especially the on-premise installed fixed position multi-camera configurations, benefit greatly from recent advances. In this paper, we propose a Multi-Camera Multi-Target (MCMT) vehicle tracking system using a constrained hierarchical clustering solution, which improves trajectory matching, and thus provides a more robust tracking of objects transitioning between cameras. YOLOv5, ByteTrack, and ResNet50-IBN ReID networks are used for vehicle detection and tracking. Static attributes such as vehicle type and vehicle color are determined from ReID features with SVM. The proposed ReID feature-based attribute categorization shows better performance, than its pure CNN counterpart. Single-camera trajectories (SCTs) are combined into multi-camera trajectories (MCTs) using hierarchical agglomerative clustering (HAC) with time and space constraints (our proposed algorithm is denoted by MCT#MAC). Similarities between SCTs are measured by comparing the mean ReID features cumulated on the trajectory. The system was evaluated on more datasets, and our experiments demonstrate that constraining HAC by manipulating the proximity matrix greatly improves the multi-camera IDF1 score.

Crashworthiness analysis based on vehicle color and driver perception

Road safety has been of increasing concern in recent years, and vehicle crashworthiness has been highlighted as a significant challenge. While known factors such as improper driving, road conditions and driving under the influence of substances have contributed to this problem, a new perspective has emerged regarding the color of cars and its impact on crash rates. Recent research has indicated that the color of a vehicle can play an important role in road safety. The colors red, silver, white and black have been identified as those that drivers perceive most clearly in different scenarios, such as time of day, distance and traffic location.

Vehicle detection system based on shape, color, and time-motion

<span lang="EN-US">Vehicle detection application can assist in-vehicle surveillance functions and have implications for various fields. A vehicle can be identified through the license number attached to its license plate, the color and its shape. Vehicle detection can make use of multimedia sensors so that the design and detection performances can be optimal. Sensor performances are influenced by factors such as the number of multimedia sensors, sensor placement, sensor positioning, and schemes in case of system failure. This study makes use of multimedia sensors with cameras equipped by a phase detection auto focus (PDAF) technology which is like a pair of eyes to see an object. This study analyses 134 vehicles with number detection and various colors to see the effect on the detection and recognition processes. The cars were passed through the camera 10 times at a speed of 10-15 km/hour with various camera distances and positions. Various values and depths of the images were generated. The farther the distance the higher the disparity values. For maximum distance of 50 m, disparity is 6.20×106 and image depth is 16.88×109. Vehicle color influences detection with orange has the best accuracy, but the gray has the largest path error value.</span>

Training and Synthesis Based Vehicle Color Conver sion Technique Using GAN

Training and Synthesis Based Vehicle Color Conver sion Technique Using GAN

Vehicle Tracking Algorithm Based on Deep Learning in Roadside Perspective

Traffic intelligence has become an important part of the development of various countries and the automobile industry. Roadside perception is an important part of the intelligent transportation system, which mainly realizes the effective perception of road environment information by using sensors installed on the roadside. Vehicles are the main road targets in most traffic scenes, so tracking a large number of vehicles is an important subject in the field of roadside perception. Considering the characteristics of vehicle-like rigid targets from the roadside view, a vehicle tracking algorithm based on deep learning was proposed. Firstly, we optimized a DLA-34 network and designed a block-N module, then the channel attention and spatial attention modules were added in the front of the network to improve the overall feature extraction ability and computing efficiency of the network. Next, the joint loss function was designed to improve the intra-class and inter-class discrimination ability of the tracking algorithm, which can better discriminate objects of similar appearance and the color of vehicles, alleviate the IDs problem and improve algorithm robustness and the real-time performance of the tracking algorithm. Finally, the experimental results showed that the method had a good tracking effect for the vehicle tracking task from the roadside perspective and could meet the practical application demands of complex traffic scenes.

Effective Two-Lane Traffic Management at the University of Ibadan, Nigeria Main Gate Using Multiple Vehicle Recognition Systems

Traffic congestion and management have posed a major challenge to many cities in the world. Intelligent traffic management system plays an important role in monitoring and enforcing traffic laws with reduced labor. This paper uses vehicle information recognition to identify unpermitted lane shunting at the University of Ibadan main gate. The vehicle recognition system captures three main details of the vehicle; its license plate, make, and colour to ensure the system, which is named UiScope, is robust enough. Machine learning and deep learning algorithms including Convolutional Neural Network (CNN), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms are used to train classifiers for vehicle make, license plate, and colour recognition. The captured details are uploaded on a Structured Query Language (SQL) database to create a blacklist of vehicles that are shunted. The querying of the database is used to determine the shunted vehicle. The success rate for plate identification is 92%, character segmentation is 87%, character recognition is 75%, and vehicle colour recognition is 78%.

Vehicle color recognition based on smooth modulation neural network with multi-scale feature fusion

Vehicle color recognition based on smooth modulation neural network with multi-scale feature fusion

Joint Semantic Intelligent Detection of Vehicle Color under Rainy Conditions

Color is an important feature of vehicles, and it plays a key role in intelligent traffic management and criminal investigation. Existing algorithms for vehicle color recognition are typically trained on data under good weather conditions and have poor robustness for outdoor visual tasks. Fine vehicle color recognition under rainy conditions is still a challenging problem. In this paper, an algorithm for jointly deraining and recognizing vehicle color, (JADAR), is proposed, where three layers of UNet are embedded into RetinaNet-50 to obtain joint semantic fusion information. More precisely, the UNet subnet is used for deraining, and the feature maps of the recovered clean image and the extracted feature maps of the input image are cascaded into the Feature Pyramid Net (FPN) module to achieve joint semantic learning. The joint feature maps are then fed into the class and box subnets to classify and locate objects. The RainVehicleColor-24 dataset is used to train the JADAR for vehicle color recognition under rainy conditions, and extensive experiments are conducted. Since the deraining and detecting modules share the feature extraction layers, our algorithm maintains the test time of RetinaNet-50 while improving its robustness. Testing on self-built and public real datasets, the mean average precision (mAP) of vehicle color recognition reaches 72.07%, which beats both sate-of-the-art algorithms for vehicle color recognition and popular target detection algorithms.

Rain Rendering and Construction of Rain Vehicle Color-24 Dataset

The fine identification of vehicle color can assist in criminal investigation or intelligent traffic management law enforcement. Since almost all vehicle-color datasets that are used to train models are collected in good weather, the existing vehicle-color recognition algorithms typically show poor performance for outdoor visual tasks. In this paper we construct a new RainVehicleColor-24 dataset by rain-image rendering using PS technology and a SyRaGAN algorithm based on the VehicleColor-24 dataset. The dataset contains a total of 40,300 rain images with 125 different rain patterns, which can be used to train deep neural networks for specific vehicle-color recognition tasks. Experiments show that the vehicle-color recognition algorithms trained on the new dataset RainVehicleColor-24 improve accuracy to around 72% and 90% on rainy and sunny days, respectively. The code is available at humingdi2005@github.com.

Automatic Street Lighting System with Vehicle Detection using Deep-Learning Based Remote Sensing

Automated street lightning is advantageous to society as it decreases the rate of accidents, vandalism and street crimes. The ability to detect vehicles and smartly manage the street light system is among the major duties of electrical distribution companies. To recognize an objects of interest in an image by a classical technique called object detection. In order to recover the enactment and reduce the complexity of object detection, numerous computer vision methods have been proposed over the past decade years. Object detection has a wide variety of applications including vehicle detection especially in remote sensing applications. Vigorous and accurate detection of vehicles for such solicitations is a moderately stimulating problem because of discrepancy of color, size, aspect ratio and alignment of vehicles and complex backgrounds of satellite images. Nevertheless, the modern deep learning-based object detection frameworks and convolutional neural networks have great potential for improving the performance of vehicle detection methods in terms of exactness, sturdiness and detection time. This paper will review both conventional and modern object detection systems. Assortment of the supreme appropriate set of object sensor, mainstay story abstraction system, train-test proportion and hyper-parameters for recognition of automobiles for nifty street light system are studied in detail and compared by deep learning based object detectors Single shot multibox detector (SSD) and You Only Look Once (YOLO) and feature abstraction systems counting Alexnet, VGG-16, Resnet-18, Resnet-50. As between accuracy and detection time these methods offer several trade-off options. On mean average precision (mAP), precision vs, recall curves, computational complexity and time complexity is established by relative investigation.

IoT Based Signal Violated Vehicle Detector

Abstract: The number of new vehicles on the road is increasing rapidly, which in turn causes highly congested roads and due to the long awaiting red signals, few vehicle drivers ignore traffic rules by violating traffic signals. This leads to a high number of road accidents and hence it is essential to detect the signal violated vehicles automatically. Traffic violation detection systems using IOT technology can able to send violated vehicle information to the concern traffic police station automatically by which the vehicle can be penalized immediately. The proposed system can be implemented using vehicle ID card installed in the vehicle itself such that if the vehicle moves further during presence of red signal, automatically, vehicle information in the form of vehicle registration number, type of vehicle (weather it is two-wheeler, car, truck, auto, etc.), owner name, vehicle colour, etc, can be forwarded to the concern authorities through concern mobile phones. To prove the concept practically, all vehicles must be equipped with these low-cost wireless ID cards such that the vehicle data can be transmitted continuously. The demo module contains a mini automatic traffic signal post along with simulated road. The system is designed such that when red signal is energized and during this period, if any vehicle crosses the zebra lines, the system receives the vehicle data and transmits the same through Wi-Fi module. If anybody violates the traffic signal, immediately alarm will be energized to alert the nearest traffic police. The main processor must be installed near the traffic signals, which regrets to acquire vehicle information during the presence of yellow and green signals. The demo module contains one toy car which will be equipped with its wireless ID card. Major building blocks: Simulation of single lane road constructed with traffic signal post & zebra crossing, main processor designed with Arduino MCU, wireless ID card designed with 89C2051 controller chip, IR signal decoder (TSOP1738), Wi – Fi module, Alarm, IR LED, 5v Power supply unit, Toy car, etc.

Multi-attribute adaptive aggregation transformer for vehicle re-identification

With the continuous development of intelligent transportation systems, vehicle-related fields have emerged a research boom in detection, tracking, and retrieval. Vehicle re-identification aims to judge whether a specific vehicle appears in a video stream, which is a popular research direction. Previous researches have proven that the transformer is an efficient method in computer vision, which treats a visual image as a series of patch sequences. However, an efficient vehicle re-identification should consider the image feature and the attribute feature simultaneously. In this work, we propose a vehicle attribute transformer (VAT) for vehicle re-identification. First, we consider color and model as the most intuitive attributes of the vehicle, the vehicle color and model are relatively stable and easy to distinguish. Therefore, the color feature and the model feature are embedded in a transformer. Second, we consider that the shooting angle of each image may be different, so we encode the viewpoint of the vehicle image as another additional attribute. Besides, different attributes are supposed to have different importance. Based on this, we design a multi-attribute adaptive aggregation network, which can compare different attributes and assign different weights to the corresponding features. Finally, to optimize the proposed transformer network, we design a multi-sample dispersion triplet (MDT) loss. Not only the hardest samples based on hard mining strategy, but also some extra positive samples and negative samples are considered in this loss. The dispersion of multi-sample is utilized to dynamically adjust the loss, which can guide the network to learn more optimized division for feature space. Extensive experiments on popular vehicle re-identification datasets verify that the proposed method can achieve state-of-the-art performance.

Tools used in the Life Cycle of Data Science and their Features

Automated street lightning is advantageous to society as it decreases the rate of accidents, vandalism and street crimes. The ability to detect vehicles and smartly manage the street light system is among the major duties of electrical distribution companies. To recognize an objects of interest in an image by a classical technique called object detection. In order to recover the enactment and reduce the complexity of object detection, numerous computer vision methods have been proposed over the past decade years. Object detection has a wide variety of applications including vehicle detection especially in remote sensing applications. Vigorous and accurate detection of vehicles for such solicitations is a moderately stimulating problem because of discrepancy of color, size, aspect ratio and alignment of vehicles and complex backgrounds of satellite images. Nevertheless, the modern deep learning-based object detection frameworks and convolutional neural networks have great potential for improving the performance of vehicle detection methods in terms of exactness, sturdiness and detection time. This paper will review both As a result of digitization, data sets are fast expanding in various ways. When there are enormous amounts of data or information groups that are complicated in nature, typical data processing methods are unable to handle them. Researchers, scientists, businesses, government entities, advertising companies, and medical researchers all face more challenges when collecting and analyzing for decision-making. The data that is accessible for research must be analyzed utilizing a variety of data analytics methods. These solutions aid in dealing with large volumes of unstructured, organized, or semi-structured data material that is constantly changing and impossible to process using traditional database management tools. This paper goes over the most common uses of data analysis tools, as well as their characteristics towards data validation. This paper presents a review and analysis of various existing ways to supporting data analysis tools for users, with a focus on identifying critical qualities. Weaknesses, opportunities, and uses will be examined in future research.

Multi‐label based view learning for vehicle re‐identification

Due to the high similarity of different vehicles with similar appearances and the great diversity of camera viewpoints, vehicle re-identification (ReID) is still a challenging task. It commonly maps query set image into a high dimensional embedding space and then retrieve gallery vehicle images according to the distance. In this letter, a multi-label based view learning (MLVL) model to enhance the distinguishability of intra-class and inter-class through multiple labels learning, and decrease the distance of intra-class features caused by multi-view appearances of vehicles. Specifically, the model includes two main parts. First, the vehicle orientation estimation module is responsible for detecting key-points of the vehicle and predicting its orientation, which provides the orientation label for the multi-label based learning network to enhance view-invariant representation. Second, a novel dual-triplet and quadruplet loss function is designed to optimise intra-class and inter-class distance with the help of multi-label information, i.e. vehicle colour, type and orientation. In particular, a multi-label based sampler is proposed to generate training mini-batches instead of random sampler. Extensive experiments results show that the proposed MLVL model achieves 5.6 % $\%$ mAP improvement on VeRi-776 and 1.9 % $\%$ on VERI-Wild datasets compared with baseline model.

Artificial Intelligence for Text-Based Vehicle Search, Recognition, and Continuous Localization in Traffic Videos

The concept of searching and localizing vehicles from live traffic videos based on descriptive textual input has yet to be explored in the scholarly literature. Endowing Intelligent Transportation Systems (ITS) with such a capability could help solve crimes on roadways. One major impediment to the advancement of fine-grain vehicle recognition models is the lack of video testbench datasets with annotated ground truth data. Additionally, to the best of our knowledge, no metrics currently exist for evaluating the robustness and performance efficiency of a vehicle recognition model on live videos and even less so for vehicle search and localization models. In this paper, we address these challenges by proposing V-Localize, a novel artificial intelligence framework for vehicle search and continuous localization captured from live traffic videos based on input textual descriptions. An efficient hashgraph algorithm is introduced to compute valid target information from textual input. This work further introduces two novel datasets to advance AI research in these challenging areas. These datasets include (a) the most diverse and large-scale Vehicle Color Recognition (VCoR) dataset with 15 color classes—twice as many as the number of color classes in the largest existing such dataset—to facilitate finer-grain recognition with color information; and (b) a Vehicle Recognition in Video (VRiV) dataset, a first of its kind video testbench dataset for evaluating the performance of vehicle recognition models in live videos rather than still image data. The VRiV dataset will open new avenues for AI researchers to investigate innovative approaches that were previously intractable due to the lack of annotated traffic vehicle recognition video testbench dataset. Finally, to address the gap in the field, five novel metrics are introduced in this paper for adequately accessing the performance of vehicle recognition models in live videos. Ultimately, the proposed metrics could also prove intuitively effective at quantitative model evaluation in other video recognition applications. T One major advantage of the proposed vehicle search and continuous localization framework is that it could be integrated in ITS software solution to aid law enforcement, especially in critical cases such as of amber alerts or hit-and-run incidents.

Policy innovation through noise in implementation: Best to be grey (or silver) on Friday, in Halifax

AbstractWhen the implementation of regulations requires judgement, there is bound to be noise in the application of the rules, but is there bias in the noise such that policy innovation, in effect, occurs in implementation? We use a recently available large postcode data set on the MOT road safety testing in the UK to answer this question. There is significant bias: the probability of failing varies systematically across postcodes, day of the week and vehicle colour. A national policy is undermined by this variability and we suggest how policy might be adjusted to reduce this unintended policy innovation.