vehicle classification
204009099
Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey
A traffic monitoring system is an integral part of Intelligent Transportation Systems (ITS) It is one of the critical transportation infrastructures that transportation agencies invest a huge amount of money to collect and analyze the traffic data to better utilize the roadway systems, improve the safety of transportation, and establish future transportation plans. With recent advances in MEMS, machine learning, and wireless communication technologies, numerous innovative traffic monitoring systems have been developed. In this article, we present a review of state of the art traffic monitoring systems focusing on the major functionality vehicle classification. We organize various vehicle classification systems, examine research issues and technical challenges, and discuss hardware/software design, deployment experience, and system performance of vehicle classification systems. Finally, we discuss a number of critical open problems and future research directions in an aim to provide valuable resources to academia, industry, and government agencies for selecting appropriate technologies for their traffic monitoring applications.
IEEE Access
Myounggyu Won
171096645
Dual Cross Entropy Loss for Small Sample Fine Grained Vehicle Classification
Fine grained vehicle classification is a challenging topic in computer vision due to the high intraclass variance and low interclass variance. Recently, considerable progress has been made in fine grained vehicle classification due to the huge success of deep neural networks. Most studies of fine grained vehicle classification based on neural networks, focus on the neural network structure to improve the classification performance. In contrast to existing works on fine grained vehicle classification, we focus on the loss function of the neural network. We add a regularization term to the cross entropy loss and propose a new loss function, Dual Cross Entropy Loss. The regularization term places a constraint on the probability that a data point is assigned to a class other than its ground truth class, which can alleviate the vanishing of the gradient when the value of the cross entropy loss is close to zero. To demonstrate the effectiveness of our loss function, we perform two sets of experiments. The first set is conducted on a small sample fine grained vehicle classification dataset, the Stanford Cars 196 dataset. The second set is conducted on two small sample datasets, the LabelMe dataset and the UIUC Sports dataset, as well as on one large sample dataset, the CIFAR 10 dataset. The experimental results show that the proposed loss function improves the fine grained vehicle classification performance and has good performance on three other general image classification tasks.
IEEE Transactions on Vehicular Technology
Xiaoxu Li Liyun Yu Dongliang Chang Zhanyu Ma Jie Cao
86674074
Fine Grained Vehicle Classification With Channel Max Pooling Modified CNNs
Convolutional neural networks (CNNs) have recently shown excellent performance on the task of fine grained vehicle classification, where the motivation is to identify the fine grained categories of the given vehicles. Generally speaking, the main motivation of the conventional back propagation algorithm is to optimize the loss function. The algorithm itself does not guarantee if the extracted features are discriminative for the task of classification. Intuitively, if we can learn more discriminative features with a relatively small number of feature maps, the generalization ability of the CNNs will be significantly improved. Therefore, we propose a channel max pooling (CMP) scheme, where a new layer is inserted between the fully connected layers and the convolutional layers. The proposed CMP scheme divides the feature maps into to several sub groups. Then, it compresses the feature maps within each sub group into a new one. The compression is carried out by selecting the maximum value among the same locations from different feature maps. Moreover, the proposed CMP layer has the advantage that it can reduce the number of parameters via reducing the number of channels in the CNNs. Experimental results on two fine grained vehicle datasets demonstrate that the CMP modified CNNs can improve the classification accuracies on the task of fine grained vehicle classification while a massive amount of parameters are reduced. Moreover, it has competitive performance when comparing with the state of the art methods.
IEEE Transactions on Vehicular Technology
Zhanyu Ma Dongliang Chang Jiyang Xie Yifeng Ding Shaoguo Wen Xiaoxu Li Zhongwei Si Jun Guo
219178943
A deep convolutional neural network based approach for vehicle classification using large scale GPS trajectory data
Abstract Transportation agencies are starting to leverage increasingly available GPS trajectory data to support their analyses and decision making. While this type of mobility data adds significant value to various analyses, one challenge that persists is lack of information about the types of vehicles that performed the recorded trips, which clearly limits the value of trajectory data in transportation system analysis. To overcome this limitation of trajectory data, a deep Convolutional Neural Network for Vehicle Classification (CNN VC) is proposed to identify the vehicle's class from its trajectory. This paper proposes a novel representation of GPS trajectories, which is not only compatible with deep learning models, but also captures both vehicle motion characteristics and roadway features. To this end, an open source navigation system is also exploited to obtain more accurate information on travel time and distance between GPS coordinates. Before delving into training the CNN VC model, an efficient programmatic strategy is also designed to label large scale GPS trajectories by means of vehicle information obtained through Virtual Weigh Station records. Our experimental results reveal that the proposed CNN VC model consistently outperforms both classical machine learning algorithms and other deep learning baseline methods. From a practical perspective, the CNN VC model allows us to label raw GPS trajectories with vehicle classes, thereby enriching the data and enabling more comprehensive transportation studies such as derivation of vehicle class specific origin destination tables that can be used for planning.
Sina Dabiri Nikola Markovic Kevin Heaslip Chandan K Reddy
197642236
Physical Features and Deep Learning based Appearance Features for Vehicle Classification from Rear View Videos
Currently, there are many approaches for vehicle classification, but there is no specific study on automated, rear view, and video based robust vehicle classification. The rear view is important for intelligent transportation systems since not all states in the United States require a frontal license plate on a vehicle. The classification of vehicles, from their rear views, is challenging since vehicles have only subtle appearance differences and there are changing illumination conditions and the presence of moving shadows. In this paper, we present a novel multi class vehicle classification system that classifies a vehicle into one of four possible classes (sedan, minivan, SUV, and a pickup truck) from its rear view video, using physical and visual features. For a given geometric setup of the camera on highways, we make physical measurements on a vehicle. These measurements include visual rear ground clearance, the height of the vehicle, and the distance between the license plate and the rear bumper. We call these distances as the physical features. The visual features, also called appearance based features, are extracted using convolutional neural networks from the input images. We achieve a classification accuracy of 93.22% and 91.52% using physical and visual features, respectively. Furthermore, we achieve a higher classification accuracy of 94.81% by fusing both the features together. The results are shown on a dataset consisting of 1831 rear view videos of vehicles and they are compared with various approaches, including deep learning techniques.
IEEE Transactions on Intelligent Transportation Systems
Rajkumar Theagarajan Ninad Thakoor Bir Bhanu
215408516
Doppler Spectrum Feature Based Human Vehicle Classification Scheme Using Machine Learning for an FMCW Radar Sensor
In this paper, we propose a Doppler spectrum feature based human vehicle classification scheme for an FMCW (frequency modulated continuous wave) radar sensor. We introduce three novel features referred to as the scattering point count, scattering point difference, and magnitude difference rate features based on the characteristics of the Doppler spectrum in two successive frames. We also use an SVM (support vector machine) and BDT (binary decision tree) for training and validation of the three aforementioned features. We measured the signals using a 24 GHz FMCW radar front end module and a real time data acquisition module and extracted three features from a walking human and a moving vehicle in the field. We then repeatedly measured the classification decision rate of the proposed algorithm using the SVM and BDT, finding that the average performance exceeded 99% and 96% for the walking human and the moving vehicle, respectively.
Sensors
Eugin Hyun Youngseok Jin
219588177
A Review on Vehicle Classification and Potential Use of Smart Vehicle Assisted Techniques
Vehicle classification (VC) is an underlying approach in an intelligent transportation system and is widely used in various applications like the monitoring of traffic flow, automated parking systems, and security enforcement. The existing VC methods generally have a local nature and can classify the vehicles if the target vehicle passes through fixed sensors, passes through the short range coverage monitoring area, or a hybrid of these methods. Using global positioning system (GPS) can provide reliable global information regarding kinematic characteristics; however, the methods lack information about the physical parameter of vehicles. Furthermore, in the available studies, smartphone or portable GPS apparatuses are used as the source of the extraction vehicle's kinematic characteristics, which are not dependable for the tracking and classification of vehicles in real time. To deal with the limitation of the available VC methods, potential global methods to identify physical and kinematic characteristics in real time states are investigated. Vehicular Ad Hoc Networks (VANETs) are networks of intelligent interconnected vehicles that can provide traffic parameters such as type, velocity, direction, and position of each vehicle in a real time manner. In this study, VANETs are introduced for VC and their capabilities, which can be used for the above purpose, are presented from the available literature. To the best of the authors' knowledge, this is the first study that introduces VANETs for VC purposes. Finally, a comparison is conducted that shows that VANETs outperform the conventional techniques.
Sensors
Hoofar Shokravi Hooman Shokravi Norhisham Bakhary Mahshid Heidarrezaei Seyed Saeid Rahimian Koloor Michal Petru
220605525
Improved Shadow Removal Algorithm for Vehicle Classification in Traffic Surveillance System
Shadows are among the most critical problems for traffic surveillance systems (TSSs) In a TSS, shadow regions significantly affect the extraction of vehicles' attributes for vehicle detection, classification and tracking. Although many methods have been proposed to address this key problem, the dilemma of accurate shadow removal with vehicles' boundaries recovery and real time processing still poses as a great challenge. In this paper, we propose a new method for shadow removal that utilizes edge features to eliminate shadows, and to refine vehicles' images regardless of the changes in illumination and shadow orientations. Experiments were done on real world data to compare the results of our method with previous ones. Thorough investigation shows that our method gets rid of vehicles' shadows more accurately and significantly restores conveyances' images from shadow separation. In addition, our method is real time.
2020 RIVF International Conference on Computing and Communication Technologies (RIVF)
Hung Ngoc Phan Long Hoang Pham Nhat Minh Chung Synh Viet-Uyen Ha
229426534
Vehicle Classification, Structure and Layouts
G K Awari Vishal Kumbhar R B Tirpude
214390427
Front and Rear Vehicle Classification
Sara Baghdadi Noureddine Aboutabit