Vehicle Recognition Method Research Articles

A Comprehensive Machine Learning Approach for Advanced Vehicle Detection and Counting

The exponential rise of urban areas and the associated surge in transportation congestion. Consequently, this study offers a thorough method for vehicle recognition and counting via the use of machine learning, as well as an effective system for real-time traffic monitoring, with the aim of reducing traffic. The first step is to develop a model that can identify and follow moving cars in still photos or video. This research delves into the topic of teaching a computer to count automobiles using machine learning, a kind of artificial intelligence. The purpose of this study is to provide a computational model for intelligent vehicle detection and tracking at a given location and time of day, using real-time images of passing cars. This approach uses OpenCV to evaluate the model's car detection and counting capabilities, and convolutional neural networks (CNNs) for object recognition and classification. The techniques laid the groundwork for early improvements, which were often enhanced using machine learning classifiers such as random forests and support vector machines (SVMs). To automate the process and get useful information regarding traffic patterns and management.

Vehicle detection in complex scenarios based on YOLOv5

With the acceleration of urbanization, traffic congestion, accident risk, and other issues have become increasingly prominent, so vehicle recognition and optimization technology has become particularly important. Based on the YOLOv5 deep learning model, this study proposes a vehicle recognition and optimization method to address key issues in urban traffic management and intelligent driving. This study first tested YOLOv5 and trained and optimized the YOLOv5 model to enhance the model's ability to detect vehicles in various complex scenarios, such as rainy days and the coexistence of people and vehicles, which have a large number of interference factors. Through experimental verification, our improved model improves the accuracy of detection in such scenarios in vehicle recognition tasks, and finds the deficiencies in the experiment and proposes corresponding solutions.

An efficient fine-grained vehicle recognition method based on part-level feature optimization

This paper presents an effective method for strengthening the discriminative ability of high-level deep features by enhancing and aggregating discriminative part-level features for the fine-grained vehicle recognition task. In general, the task of visual recognition concentrates more on the visual differences at the object level. However, for fine-grained object recognition, the visual differences between target objects typically exist in local discriminative areas, so it is more concerned about extracting fine-grained features from these part regions. In this context, we propose solving this issue with a novel feature extraction method from two perspectives: the generation of more feature descriptors of part regions through the learning process of deep networks and the aggregation of part-level discriminative features. This approach is designed to improve the backbone networks to generate finer-level part features through a part-level feature enhancement module and to investigate the intrinsic part-level features of the backbone networks with the help of a feature aggregation module. The enhancement module efficiently finds the finer features highly correlated to the part regions. Then the feature aggregation module builds correlations of similar part features through feature grouping and fusion. Moreover, our proposed method does not require additional parts annotations and achieves comparable performance on two widely-used benchmarks for recognizing fine-grained vehicle types. Experimental results and explainable visualizations demonstrate the effectiveness of the proposed method.

Research on a Vehicle Recognition Method Based on Radar and Camera Information Fusion

To improve the accuracy and real-time performance of vehicle recognition in an advanced driving-assistance system (ADAS), a vehicle recognition method based on radar and camera information fusion is proposed. Firstly, the millimeter-wave radar and camera are calibrated jointly, the radar recognition information is mapped on the camera image, and the region of interest is established. Then, based on operator edge detection, global threshold binarization is performed on the image of the region of interest (ROI) to obtain the contour information of the vehicle in front, and Hough transform is used to fit the vehicle contour edge straight line. Finally, a sliding window is established according to the symmetry characteristics of the fitting line, which can find the vehicle region with the highest symmetry and complete the identification of the vehicle. The experimental results show that compared to the original recognition region of the radar, the mean square error of this algorithm is reduced by 13.4 and the single frame detection time is reduced to 28 ms. It is proven that the algorithm has better accuracy and a faster detection rate, and it can solve the problem of an inaccurate recognition region caused by radar error.

Vehicle Recognition from Unmanned Aerial Vehicle Videos Based on Fusion of Target Pre-Detection and Deep Learning

For accurate and effective automatic vehicle identification, morphological detection and deep convolutional networks were combined to propose a method for locating and identifying vehicle models from unmanned aerial vehicle (UAV) videos. First, the region of interest of the video frame image was sketched and grey-scale processing was performed; sub-pixel-level skeleton images were generated based on the Canny edge detection results of the region of interest; then, the image skeletons were decomposed and reconstructed. Second, a combination of morphological operations and connected domain morphological features were applied for vehicle target recognition, and a deep learning image benchmark library containing 244,520 UAV video vehicle samples was constructed. Third, we improved the AlexNet model by adding convolutional layers, pooling layers, and adjusting network parameters, which we named AlexNet*. Finally, a vehicle recognition method was established based on a candidate target extraction algorithm with AlexNet*. The validation analysis revealed that AlexNet* achieved a mean F1 of 85.51% for image classification, outperforming AlexNet (82.54%), LeNet (63.88%), CaffeNet (46.64%), VGG16 (16.67%), and GoogLeNet (14.38%). The mean values of Pcor, Pre, and Pmiss for cars and buses reached 94.63%, 6.87%, and 4.40%, respectively, proving that this method can effectively identify UAV video targets.

A novel part-level feature extraction method for fine-grained vehicle recognition

In this paper, we propose a novel part-level feature extraction method to enhance the discriminative ability of deep convolutional features for the task of fine-grained vehicle recognition. Generally, the challenges for fine-grained vehicle recognition are mainly caused by the subtle visual differences between part regions of vehicles. Therefore, it is essential to extract discriminative features from part regions. Many existing methods, especially deep convolutional neural networks (D-CNNs), tend to detect the discriminative part regions explicitly or learn the part information implicitly through network restructuring and neglect the abundant part-level information contained in the high-level features generated by CNNs. In light of this, we propose a simple and effective part-level feature extraction method to enhance the representation of part-level features within the global features of target object generated by the backbone networks. The proposed method is built on the deep convolutional layers from which the discriminative part features could be integrated and extracted accordingly. More specifically, a basic feature grouping module is adopted to integrate the feature maps of deep convolutional layers into groups in each of which the related discriminative parts are assembled. The feature grouping process is performed in a multi-stage manner to ensure the integration process. Then a fusion module follows to model the coarse-to-fine relationship of the part features and further ensure the integrity and effectiveness of the part features. We conduct comparison experiments on public datasets, and the results show that the proposed method achieves comparable performance with state-of-the-art algorithms.

Detection of Logos of Moving Vehicles under Complex Lighting Conditions

This study proposes a method for vehicle logo detection and recognition to detect missing and inaccurate vehicle marks under complex lighting conditions. For images acquired in complex light conditions, adaptive image enhancement is used to improve the accuracy of car sign detection by more than 2%; for the problems of multi-scale and detection speed of vehicle logo recognition in different images, the paper improves the target detection algorithm to improve the detection accuracy by more than 3%. The adaptive image enhancement algorithm and improved You Only Look One-level Feature (YOLOF) detection algorithm proposed in this study can effectively improve the correct identification rate under complex lighting conditions.

MASFF: Multiscale Adaptive Spatial Feature Fusion Method for Vehicle Recognition

<p>Traditional vehicle recognition methods have the disadvantages such as low efficiency and time-consuming due to the complex background and overlapping situation. In this paper, we propose a multiscale adptive spatial feature fusion (ASFF) method for vehicle recognition. First, it calculates the difference hash values of images. Then the hash value is used to judge the similarity between the current frame and the previous frame. When the similarity is less than the threshold value, it is input to ResNet18 model for detection. Using ResNet18 as the base network can reduce network parameters. Then, aiming at the problem that the detection effect of ASFF for vehicle recognition is not ideal, the offset loss and width-height loss are replaced by the intersection ratio loss. Meanwhile, multi-scale adaptive spatial feature fusion method is adopted to fuse the multi-level features of the network. The experimental results show that the average accuracy with proposed methed is increased by 2.1%. For BDD100K and Pascal VOC datasets, the average accuracy of predicted borders is increased by 5.5%, when the IoU is greater than 0.5. With the GTX1060Ti, the recognition speed can reach 149 frames per second. The multiscale ASFF in this paper can significantly improve the vehicle recognition accuracy.</p> <p> </p>

DFT: A deep feature‐based semi‐supervised collaborative training for vehicle recognition in smart cities

AbstractIntelligent transport systems (ITS) is a popular field of research in smart city. Image‐based vehicle recognition is one of the most promising new techniques. The traditional appearance‐based method has some limitations in feature view. The machine learning algorithm can satisfy multiple feature views, but it mainly adopts an improved supervised learning method, which needs manual annotation and has low efficiency. The semi‐supervised collaborative training method is widely used in the image recognition process to improve machine learning algorithms' accuracy and generalization performance. This paper proposes DFT (deep feature‐based training) method for vehicle recognition in smart cities. DFT is also a semi‐supervised collaborative training method on basis of two base learners. DFT adjusts data pre‐processing and training process, optimizes the constructing a disagreement encoding network, and expands the recognition disagreement of pseudo‐labelled samples‐based training set. Compared with the typical collaborative training methods, DFT greatly accelerates the model's training process by reducing the convergence time, and improves the efficiency of vehicle recognition, while remaining the recognition accuracy unchanged.

Vehicle Recognition Method Based on Improved YOLOv3 Algorithm

Vehicle Recognition Method Based on Improved YOLOv3 Algorithm

Urban vehicle extraction from aerial laser scanning point cloud data

ABSTRACT A vehicle extraction method is proposed in this paper to extract vehicles in urban areas more accurately from airborne point clouds. First, the ground points are separated from the non-ground points, and a potential vehicle-occupied area (PVOA) is then extracted from the ground point cloud. A PVOA-based non-ground point cloud segmentation method is proposed in this work, and a gap-based method is put forward to re-cluster the segment, which may include multiple vehicles. The non-ground point cloud is clustered into a series of one-vehicle segments and empty segments. Following this, a shape-based vehicle recognition method is presented that can judge whether or not a given segment is a vehicle using a dynamic time warping similarity measurement. In addition to judging whether or not a segment is a vehicle, the category of each vehicle can also be determined. To a significant extent, our PVOA-based non-ground point cloud segmentation method can avoid the difficulties of over- and under-segmentation that arise in current mainstream methods of object-based vehicle extraction, and can also avoid incorrect segmentation in case of vehicles parked close together. Our shape-based vehicle recognition method can exclude non-vehicle objects, especially those with sizes similar to those of vehicles. This method is more effective than current algorithms based on normal geometric features such as area and rectangularity. Using four datasets of typical urban scenarios, the performance of the new algorithm is tested and compared with that of the OBPCA and DT algorithm. The experimental results show that the correctness, completeness, and quality of the new algorithm are 96.7%, 91.1%, and 93.8%, higher than that of the OBPCA and DT algorithm.

VEHICLE RECOGNITION IN AERIAL LIDAR POINT CLOUD BASED ON DYNAMIC TIME WARPING

Abstract. A two-step vehicle recognition method from an aerial Lidar point cloud is proposed in this paper. First, the Lidar point cloud is segmented using the region-growing algorithm with vehicle size limitation. Then the vehicle is recognized according to the profile shape based on dynamic time warping. The proposed method can detect vehicles parking under trees in an urban scene, and classifies the vehicles into different classes. The vehicle location, orientation, parking direction and size can also be determined. The experimental result based on a real urban Lidar point cloud shows that the proposed method can correctly recognize 95.1 % of vehicles.

A real-time multiple vehicle tracking method for traffic congestion identification

Traffic congestion is a severe problem in many modern cities around the world. Real-time and accurate traffic congestion identification can provide the advanced traffic management systems with a reliable basis to take measurements. The most used data sources for traffic congestion are loop detector, GPS data, and video surveillance. Video based traffic monitoring systems have gained much attention due to their enormous advantages, such as low cost, flexibility to redesign the system and providing a rich information source for human understanding. In general, most existing video based systems for monitoring road traffic rely on stationary cameras and multiple vehicle tracking method. However, most commonly used multiple vehicle tracking methods are lack of effective track initiation schemes. Based on the motion of the vehicle usually obeys constant velocity model, a novel vehicle recognition method is proposed. The state of recognized vehicle is sent to the GM-PHD filter as birth target. In this way, we relieve the insensitive of GM-PHD filter for new entering vehicle. Combining with the advanced vehicle detection and data association techniques, this multiple vehicle tracking method is used to identify traffic congestion. It can be implemented in real-time with high accuracy and robustness. The advantages of our proposed method are validated on four real traffic data.

Vehicle Recognition based on Pseudo invariant Linear Moment Features and ELM

of the problem of slow speed and low accuracy of the vehicle recognition in advanced driver assistance systems, a vehicle recognition method based on pseudo invariant linear moment features and ELM is proposed. Target edge is extracted by the improved PCNN model, according to the characteristic of multiple target features, the pseudo invariant linear moment features are extracted, then ELM model is used to train and recognize the databases. The validity of the model is verified through experiments, compared with other algorithms, the recognition accuracy of pseudo invariant linear moment features and ELM vehicle recognition method is higher and the speed is faster, which provides a new way to identify the vehicle in real-time monitoring system of the vehicle.

Sparse representation based SAR vehicle recognition along with aspect angle.

As a method of representing the test sample with few training samples from an overcomplete dictionary, sparse representation classification (SRC) has attracted much attention in synthetic aperture radar (SAR) automatic target recognition (ATR) recently. In this paper, we develop a novel SAR vehicle recognition method based on sparse representation classification along with aspect information (SRCA), in which the correlation between the vehicle's aspect angle and the sparse representation vector is exploited. The detailed procedure presented in this paper can be summarized as follows. Initially, the sparse representation vector of a test sample is solved by sparse representation algorithm with a principle component analysis (PCA) feature-based dictionary. Then, the coefficient vector is projected onto a sparser one within a certain range of the vehicle's aspect angle. Finally, the vehicle is classified into a certain category that minimizes the reconstruction error with the novel sparse representation vector. Extensive experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset and the results demonstrate that the proposed method performs robustly under the variations of depression angle and target configurations, as well as incomplete observation.

Vehicle Recognition Method by Image Processing with Intelligent System

Vehicle Recognition Method by Image Processing with Intelligent System

Vehicle Recognition Using Information of Shadows underneath Vehicles and Template Matching with Genetic Algorithm

A vehicle recognition method for the traffic images taken from roadside is proposed. This method detects vehicles by using information of the shadows underneath the vehicles. A template matching with genetic algorithm is used when the judgment for vehicle existence by the shadows cannot be done because of overlapping of vehicles. Experimental results show that the recognition accuracy is 94.9%.

Recognizing vehicles in infrared images using IMAP parallel vision board

Describes a method for vehicle recognition, in particular for recognizing make and model. Our system takes into account the fact that vehicles of the same make and model number come in different colors; it employs infrared (IR) images, thereby eliminating color differences. The use of IR images also enables us to use the same algorithm both day and night. This ability is particularly important because the algorithm must be able to locate many feature points, especially at night. Our algorithm is based on a configuration of local features. For the algorithm, our system first makes a compressed database of local features of a target vehicle from training images given in advance; the system then matches a set of local features in the input image with those in the training images for recognition. This method has the following three advantages: (1) it can detect even if part of the target vehicle is occluded; (2) it can detect even if the target vehicle is translated due to running out of the lanes; and (3) it does not require us to segment a vehicle part from input images. We have two implementations of the algorithm. One is referred to as the eigenwindow method, while the other is called the vector-quantization method. The former method is good at recognition, but is not very fast. The latter method is not very good at recognition but it is suitable for an IMAP parallel image-processing board; hence, it can be fast. In both implementations, the above-mentioned advantages have been confirmed by performing outdoor experiments.

Vehicles Recognition Method using Spatio-Temporal Images

The authors propose a vehicle recognition method which uses a directional temporal plane transform (DTT). The DTT transforms spatio-temporal images to 2D data on a directional-temporal plane and adaptively changes the processing areas by comparing the position of the areas with the position of 3D vehicle models.