José J. Lamas-Seco

Bio: José J. Lamas-Seco is an academic researcher from University of A Coruña. The author has contributed to research in topics: Inductive sensor & Induction loop. The author has an hindex of 3, co-authored 6 publications receiving 46 citations.

Vehicle Classification Using the Discrete Fourier Transform with Traffic Inductive Sensors

Abstract: Inductive Loop Detectors (ILDs) are the most commonly used sensors in traffic management systems. This paper shows that some spectral features extracted from the Fourier Transform (FT) of inductive signatures do not depend on the vehicle speed. Such a property is used to propose a novel method for vehicle classification based on only one signature acquired from a sensor single-loop, in contrast to standard methods using two sensor loops. Our proposal will be evaluated by means of real inductive signatures captured with our hardware prototype.

SiDIVS: Simple Detection of Inductive Vehicle Signatures with a Multiplex Resonant Sensor.

Abstract: This work provides a system capable of obtaining simultaneous inductive signatures of vehicles traveling on a roadway with minimal cost. Based on Time-Division Multiplexing (TDM) with multiple oscillators, one for each inductive loop, the proposed system detects the presence of vehicles by means of a shift in the oscillation period of the selected loop and registers the signature of the detected vehicles by measuring the duration of a fixed number of oscillator pulses. In order to test the system in an actual environment, we implement a prototype that we denote as SiDIVS (Simple Detection of Inductive Vehicle Signatures) and acquire different vehicle inductive signatures under real scenarios. We also test the robustness of the detector by simulating the effect of noise on the signature acquisition.

Multi-loop inductive sensor model for vehicle traffic applications

Abstract: In this paper we develop an accurate sensor model for inductive loop detectors which are commonly used in traffic management systems. The model is based on rectangular coils and can be readily used for the analysis of vehicle inductive signatures, but it can be easily extended to other applications such as eddy-current non-destructive testing, magnetic resonance or induction heating. Our proposed model considers both flat and non-flat vehicle profiles and exhibits better performance than the standard approach under all the tested scenarios.

System for vehicle classification: Hardware prototype and off-line signal proccesing

Abstract: This paper presents a complete system for vehicle classification composed by an inductive-loop detector and the corresponding off-line algorithms. The system detects the presence of vehicles by means of a shift in the oscillation period of the selected loop so that the signature of the detected vehicles is registered by measuring the duration of a fixed number of oscillator pulses. We focus on the open issue of counting the number of vehicles (classified into cars, vans and trucks) on a roadway. The classical method for such purpose consists of estimating the vehicle length using the inductive signatures obtained from two loops and, subsequently, it classifies them taking into account a prefixed threshold. With the goal of improving the performance, in this paper we propose a method based on classifying the vehicles with uncertainty about the decision. Such methods are evaluated using real inductive signatures captured with our prototype.

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

Abstract: 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.

Vehicle-Assisted Techniques for Health Monitoring of Bridges.

Abstract: Bridges are designed to withstand different types of loads, including dead, live, environmental, and occasional loads during their service period Moving vehicles are the main source of the applied live load on bridges The applied load to highway bridges depends on several traffic parameters such as weight of vehicles, axle load, configuration of axles, position of vehicles on the bridge, number of vehicles, direction, and vehicle’s speed The estimation of traffic loadings on bridges are generally notional and, consequently, can be excessively conservative Hence, accurate prediction of the in-service performance of a bridge structure is very desirable and great savings can be achieved through the accurate assessment of the applied traffic load in existing bridges In this paper, a review is conducted on conventional vehicle-based health monitoring methods used for bridges Vision-based, weigh in motion (WIM), bridge weigh in motion (BWIM), drive-by and vehicle bridge interaction (VBI)-based models are the methods that are generally used in the structural health monitoring (SHM) of bridges The performance of vehicle-assisted methods is studied and suggestions for future work in this area are addressed, including alleviating the downsides of each approach to disentangle the complexities, and adopting intelligent and autonomous vehicle-assisted methods for health monitoring of bridges

Vehicle Detection with Occlusion Handling, Tracking, and OC-SVM Classification: A High Performance Vision-Based System

Abstract: This paper presents a high performance vision-based system with a single static camera for traffic surveillance, for moving vehicle detection with occlusion handling, tracking, counting, and One Class Support Vector Machine (OC-SVM) classification. In this approach, moving objects are first segmented from the background using the adaptive Gaussian Mixture Model (GMM). After that, several geometric features are extracted, such as vehicle area, height, width, centroid, and bounding box. As occlusion is present, an algorithm was implemented to reduce it. The tracking is performed with adaptive Kalman filter. Finally, the selected geometric features: estimated area, height, and width are used by different classifiers in order to sort vehicles into three classes: small, midsize, and large. Extensive experimental results in eight real traffic videos with more than 4000 ground truth vehicles have shown that the improved system can run in real time under an occlusion index of 0.312 and classify vehicles with a global detection rate or recall, precision, and F-measure of up to 98.190%, and an F-measure of up to 99.051% for midsize vehicles.

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

Abstract: 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.

Vehicle Classification Using the Discrete Fourier Transform with Traffic Inductive Sensors

Abstract: Inductive Loop Detectors (ILDs) are the most commonly used sensors in traffic management systems. This paper shows that some spectral features extracted from the Fourier Transform (FT) of inductive signatures do not depend on the vehicle speed. Such a property is used to propose a novel method for vehicle classification based on only one signature acquired from a sensor single-loop, in contrast to standard methods using two sensor loops. Our proposal will be evaluated by means of real inductive signatures captured with our hardware prototype.