Hybrid three-dimensional and support vector machine approach for automatic vehicle tracking and classification using a single camera

At present, the automatic classification of vehicles on roads is mostly based on image recognition, and there are defects in adaptability under non-line-of-sight environments. In this paper, based on the similarity of the integration of the ecosystem model and multi-neural network model, an artificial neural network group (BNNG) algorithm was proposed. The vehicle’s driving acoustic signal was taken as the research object, and it was calculated using the Artificial Neural Network (BNNG) algorithm to achieve automatic classification and recognition of vehicle models. Through experimental tests, it is shown that under non-line-of-sight environments, the accuracy of vehicle classification can be improved, and the misrecognition rate of similar models can be greatly reduced. This provided a new method for the automatic classification and identification of vehicles on roads, which was of great significance to monitor vehicle safety in non-line-of-sight environments.

Support vector machine (SVM) classifiers are popular in many computer vision tasks. In most of them, the SVM classifier assumes that the object to be classified is centered in the query image, which might not always be valid, e.g., when locating and classifying a particular class of vehicles in a large scene. In this paper, we introduce a new classifier called Maximum Margin Correlation Filter (MMCF), which, while exhibiting the good generalization capabilities of SVM classifiers, is also capable of localizing objects of interest, thereby avoiding the need for image centering as is usually required in SVM classifiers. In other words, MMCF can simultaneously localize and classify objects of interest. We test the efficacy of the proposed classifier on three different tasks: vehicle recognition, eye localization, and face classification. We demonstrate that MMCF outperforms SVM classifiers as well as well known correlation filters.

The problem with development of the support vector machine (SVM) classifiers using modified particle swarm optimization (PSO) algorithm and their ensembles has been considered. Solving this problem would allow fulfilling the high-precision data classification, especially Big Data classification, with the acceptable time expenditures. The modified PSO algorithm conducts a simultaneous search of the type of kernel functions, the parameters of the kernel function and the value of the regularization parameter for the SVM classifier. The idea of particles' «regeneration» served as the basis for the modified PSO algorithm. In the implementation of this algorithm, some particles change the type of their kernel function to the one which corresponds to the particle with the best value of the classification accuracy. The offered PSO algorithm allows reducing the time expenditures for the developed SVM classifiers, which is very important for Big Data classification problem. In most cases such SVM classifier provides the high quality of data classification. In exceptional cases the SVM ensembles based on the decorrelation maximization algorithm for the different strategies of the decision-making on the data classification and the majority vote rule can be used. Also, the two-level SVM classifier has been offered. This classifier works as the group of the SVM classifiers at the first level and as the SVM classifier on the base of the modified PSO algorithm at the second level. The results of experimental studies confirm the efficiency of the offered approaches for Big Data classification.

This paper proposes an automatic vehicles counting, classification and tracking for identifying Traffic flows in an intersection which is a fundamental task for video surveillance in urban traffic management. In intelligent transportation systems (ITS), especially in field of urban traffic management, intersections monitoring is one of the critical and challenging tasks. Where, objects have different characteristics such as varying velocities, stop and go. Therefore it is necessary to use a hybrid scheme of adaptive background mixture model to learn background model faster and more accurately, instead of using single rate of adaptation, which is not adequate. The main focus of this paper is to analyze activities at intersection in number of traffic zones for counting and classifying vehicles and then tracking to extract traffic flows which assists in regulating traffic lights for using in smart cameras. Traffic zones definition in intersection video, based on trajectories clustering; greatly reduce the time and volume of computations. Smart camera's fundamental purpose is to efficiently reduce the transmission rate and also analyze an intersection scene and report statics and information of interest which is not an image. Ground-truth experiments with urban traffic sequences show that our proposed algorithm is very promising relative to results using other techniques.

Vehicle detection and classification are important tasks in the automatic traffic monitoring system. The proposed work focuses on vehicle detection and classification. Vehicle detection is carried out using the combination of dense optical flow method and integrated binary projection profile. Inception-ResNet-v2 is used as a feature extraction technique and extracted features are fed to two different classifiers such as Support Vector Machine and Random Forest to classify the vehicle type. The recognition performance of Inception-ResNet-v2 with these classifiers is significantly high and the proposed approach obtained an output accuracy as 99.89% and 98.615% in Support Vector Machine and Random forest respectively.

Traffic volume is gradually increasing due to the development of technology and the concentration of people in cities. As the results, traffic congestion and traffic accidents are becoming social problems. Detecting and tracking a vehicle based on computer vision is a great helpful in providing important information such as identifying road traffic conditions and crime situations. However, vehicle detection and tracking using a camera is affected by environmental factors in which the camera is installed. In this paper, we thus propose a deep learning based on vehicle classification and tracking scheme to classify and track vehicles in a complex and diverse environment. Using YOLO model as deep learning model, it is possible to quickly and accurately perform robust vehicle tracking in various environments, compared to the traditional method.

Vehicle detection and tracking plays a major role in military and civilian applications. The accurate detection of multiple vehicles in a complicated traffic environment is too difficult. This process is made more difficult, if there are occlusions between vehicles. The optimal feature selection of a vehicle is also a challenging task. These issues are focused in the existing work namely convolution neural network (CNN) based vehicle detection. However in this work, feature selection is not concentrated and with the lesser feature information classification accuracy is reduced considerably. This is focused and resolved in the proposed research by introducing the method namely Enhanced Convolution neural network with Support Vector Machine (ECNN-SVM) based vehicle detection. In this work initially feature extraction is done. Haar like features are used for this purpose. This has compact representation, capture information from multiple scales, encoded edge and structural information and can be efficient computation. Haar like feature pool is large scale in nature. Enhanced bat optimization is used to select features from this pool. This selection is done by combining sample’s class label and feature value. Confidence level between two thresholds of tracking rectangles is used for classification. Support vector machine (SVM) classifier is combined with local binary pattern to perform this classification. The interference areas between moving objects and vehicles are removed by Enhanced Convolutional Neural Network (ECNN) classifier. The efficiency of the proposed method is computed by using experimental and theoretical analysis.

The establishment of a comprehensive statewide traffic counting program is discussed. The program comprises automatic traffic recorder (ATR), automatic vehicle classification (AVC), and weigh-in-motion (WIM) sites for the state of Delaware. The program was undertaken to review, establish, and implement effective statistical and procedural methods. The second phase of a two phase project, which implements the methodologies that were derived in the first phase, is presented. Using descriptive analysis and seasonal grouping, the number and location of sites needed for each of the three types of traffic monitoring devices were determined. Existing field data from Delaware's current ATR locations allowed for a statistical determination of the necessary number and road-type group distribution for the ATR sites. The absence of field data for AVC and WIM sites, however, necessitated alternative methods for determining the number and location of the traffic monitoring devices. As a result, a combination of statistical analysis and engineering judgment must be used for the establishment of any statewide traffic monitoring system.

Ubiquitously deployed Internet of Things (IoT)- based automatic vehicle classification systems will catalyze data-driven traffic flow optimization in future smart cities and will transform the road infrastructure itself into a dynamically sensing Cyber-physical System (CPS). Although a wide range of different traffic sensing systems has been proposed, the existing solutions are not yet able to simultaneously satisfy the multitude of requirements, e.g., accuracy, robustness, cost-efficiency, and privacy preservation. In this paper, we present a novel approach, which exploits radio fingerprints - multidimensional attenuation patterns of wireless signals - for accurate and robust vehicle detection and classification. The proposed system can be deployed in a highly cost-efficient manner as it relies on off-the-shelf embedded devices which are installed into existing delineator posts. In a comprehensive field evaluation campaign, the performance of the radio fingerprinting-based approach is analyzed within an experimental live deployment on a German highway, where it is able to achieve a binary classification success ratio of more than 99% and an overall accuracy of 93.83% for a classification task with seven different classes.

Using information technology such as search engine for collecting and monitoring of network public opinion is a practical and effective method. This paper puts forward an improved algorithm of SVM classifier based on incremental learning online, and then implements a topic crawler system for network public opinion and to grab the public opinion.

This paper summarizes the special session, “Detection, Tracking, and Classification of Unmanned Aircraft and Underwater Vehicles,” that took place during the 5th Joint Meeting of the Acoustical Society of America and the Acoustical Society of Japan. The sessions were cosponsored by the Signal Processing in Acoustics and Underwater Acoustics Technical Committees and consisted of nine talks by government and academic researchers from institutions in the United States, Australia and South Korea. The sessions described basic and applied acoustic research to counter the threats from aerial and underwater unmanned platforms.

Traffic monitoring and parameters estimation from urban to non urban (battlefield environment) traffic is fast-emerging field based on acoustic signals. We present here a comprehensive review of the state-of-the-art acoustic signal for vehicular speed estimation, density estimation and classification, critical analysis and an outlook to future research directions. This field is of increasing relevance for intelligent transport systems (ITSs). In recent years video monitoring and surveillance systems has been widely used in traffic management and hence traffic parameters can be achieved using such systems, but installation, operational and maintenance cost associated with these approaches are relatively high compared to the use of acoustic signal which is having very low installation and maintenance cost. The classification process includes sensing unit, class definition, feature extraction, classifier application and system evaluation. The acoustic classification system is part of a multi sensor real time environment for traffic surveillance and monitoring. Classification accuracy achieved by various studied algorithms shows very good performance for the 'Heavy Weight' class of vehicles as compared to the other category Light Weight. Also a slight performance degrades as vehicle speed increases. Vehicular speed estimation corresponds to average speed and traffic density measurement, and can be substantially used for traffic signal timings optimization.

The following paper depicts an architectural layout and operational insights towards an optoelectronic system dedicated for vehicle counting and classification, designed to monitor occupancy of rest areas. The system is based upon an image cue acquisition by video cameras and advanced signal processing mechanism. In order to satisfy the appropriate realization of measurement procedure (counting and classification), a specific measurement configuration scheme alongside optimized video stream capturing parameters were set up. Concerning signal processing methods, the most effective and appropriate for the purpose of vehicle counting and classification were selected. Moreover, the article presents the outcomes of the designed measurement campaign conducted on the S7 expressway within the agenda of the National Center for Research and Development RID project. The results obtained with use of the designed methodology were compared to the outcomes of the manual method.

This paper presents a new vehicle classification and develops a traffic monitoring detector to provide reliable vehicle classification to aid traffic management systems. The basic principle of this approach is based on measuring the dynamic strain caused by vehicles across pavement to obtain the corresponding vehicle parameters – wheelbase and number of axles – to then accurately classify the vehicle. A system prototype with five embedded strain sensors was developed to validate the accuracy and effectiveness of the classification method. According to the special arrangement of the sensors and the different time a vehicle arrived at the sensors one can estimate the vehicle's speed accurately, corresponding to the estimated vehicle wheelbase and number of axles. Because of measurement errors and vehicle characteristics, there is a lot of overlap between vehicle wheelbase patterns. Therefore, directly setting up a fixed threshold for vehicle classification often leads to low-accuracy results. Using the machine learning pattern recognition method to deal with this problem is believed as one of the most effective tools. In this study, support vector machines (SVMs) were used to integrate the classification features extracted from the strain sensors to automatically classify vehicles into five types, ranging from small vehicles to combination trucks, along the lines of the Federal Highway Administration vehicle classification guide. Test bench and field experiments will be introduced in this paper. Two support vector machines classification algorithms (one-against-all, one-against-one) are used to classify single sensor data and multiple sensor combination data. Comparison of the two classification method results shows that the classification accuracy is very close using single data or multiple data. Our results indicate that using multiclass SVM-based fusion multiple sensor data significantly improves the results of a single sensor data, which is trained on the whole multisensor data set.

This paper describes the 1990 test of an automatic vehicle classification (AVC) system employing an inductance detector loop and photoelectric sensors emitting infrared-spectrum light beams, which were reflected off commonly used raised traffic lane markers. The passing of a vehicle activated the sensors. Signals from the activation go through a special microprocessor into a personal computer for interpretation, display, and recording. Vehicle speed and axle patterns are inferred from the pattern of the photoelectric beam interruptions. The system is tested several times at a high-speed (50–65 mph) Oklahoma turnpike site by comparing the automatic classifications with classifications by a person. The researchers use computer printouts and written field observations to evaluate the number and type of errors. An error is said to occur when the automatic vehicle classification does not agree with the classification by the observer. The evaluation shows that the first-generation AVC system correctly classifies about 95% of 1,736 observed vehicles. Further analysis of the raw data and the errors found therein suggests that, with modifications, the AVC system can correctly classify about 97–98% of the vehicles. Snow or heavy rain does not occur during the tests, so ability to classify in these conditions is not evaluated.