Vehicle type classification from laser scans with global alignment kernels

Chidlovskii, Boris; Rodríguez-Serrano, José A.

doi:10.1109/itsc.2014.6958145

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…(Sandhawalia, Rodriguez-Serrano, Poirier & Csurka, 2013) proposes a comparison between three different features but using a linear classifier engine; this work reports the following classification rates for each type of feature: Raw Profiles Features 79.93%, Fisher Laser Signatures 70.52%, and Fisher Image Signatures 83.04%. (Chidlovskii, Csurka & Rodriguez-Serrano, 2014)…”

Section: Classification Resultsmentioning

confidence: 99%

“…In this work, the average rate classification performances for the FPFH, SHOT and NARF descriptors were 84.52%, 81.5% and 79.18% respectively, which are comparable with current state of the art methods. Other AVC systems focus on increasing feature descriptiveness or having powerful classifiers, which is the case of (Urazghildiiev, Ragnarsson, Ridderstrom, Rydberg, Ojefors, Wallin, Enochsson, Ericson, & Lofqvist, 2007), (Sandhawalia, Rodriguez-Serrano, Poirier & Csurka, 2013) and (Chidlovskii, Csurka & Rodriguez-Serrano, 2014). This work was conceived enhancing both using high descriptive point cloud features and high generalization capability on classifier.…”

Section: Discussionmentioning

confidence: 99%

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Clasificación de vehículos basada en bolsa de palabras visuales y usando imágenes de rango

Hernández¹,

Gómez

Bacca-Cortes

2015

inycomp

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Los descriptores de características en 3D extraídos de nubes de puntos se han convertido en una fuente deinformación promisoria para muchas aplicaciones. Estas aplicaciones incluyen reconocimiento de objetos o formas,modelamiento de edificaciones, modelamiento de estructuras civiles, sistemas de navegación autónoma, etc.Considerando estas tendencias, este artículo presenta un sistema de clasificación de vehículos, basado en bolsade palabras, el cual extrae descriptores de características de imágenes de rango capturadas usando un láser SICKLMS200. Nuestro enfoque usa información visual para estimar la velocidad de los vehículos usando un filtro deKalman, esta estimación de la velocidad es usada para registrar los datos del láser y construir la nube de puntosde la escena. En este trabajo, un dataset fue capturado a partir de la nube de puntos del vehículo, su informaciónvisual, la estimación de la velocidad del vehículo y una etiqueta de la clase de vehículo. Usando este dataset, variosdescriptores de 3D fueron probados y árboles KD fueron empleados para acelerar el proceso de clasificación, esteúltimo realizado con la Bolsa de Palabras. En consecuencia, nuestro enfoque puede clasificar hasta nueve diferentesclases de vehículos; el desempeño del clasificador fue medido usando curvas tipo Precision –Recall.

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Section: Classification Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Clasificación de vehículos basada en bolsa de palabras visuales y usando imágenes de rango

Hernández¹,

Gómez

Bacca-Cortes

2015

inycomp

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“…The laser scanner is used to take the shapes of vehicles via a calculated depth map [43]. Laser scanners based on ToF or the phase-shift principle are more expensive than cameras, and laser scanners produce data of lower quality than cameras [41].…”

Section: Laser Scannermentioning

confidence: 99%

Review of IoT Sensor Systems Used for Monitoring the Road Infrastructure

Micko

Papcun

Zolotová

2023

Sensors

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An intelligent transportation system is one of the fundamental goals of the smart city concept. The Internet of Things (IoT) concept is a basic instrument to digitalize and automatize the process in the intelligent transportation system. Digitalization via the IoT concept enables the automatic collection of data usable for management in the transportation system. The IoT concept includes a system of sensors, actuators, control units and computational distribution among the edge, fog and cloud layers. The study proposes a taxonomy of sensors used for monitoring tasks based on motion detection and object tracking in intelligent transportation system tasks. The sensor’s taxonomy helps to categorize the sensors based on working principles, installation or maintenance methods and other categories. The sensor’s categorization enables us to compare the effectiveness of each sensor’s system. Monitoring tasks are analyzed, categorized, and solved in intelligent transportation systems based on a literature review and focusing on motion detection and object tracking methods. A literature survey of sensor systems used for monitoring tasks in the intelligent transportation system was performed according to sensor and monitoring task categorization. In this review, we analyzed the achieved results to measure, sense, or classify events in intelligent transportation system monitoring tasks. The review conclusions were used to propose an architecture of the universal sensor system for common monitoring tasks based on motion detection and object tracking methods in intelligent transportation tasks. The proposed architecture was built and tested for the first experimental results in the case study scenario. Finally, we propose methods that could significantly improve the results in the following research.

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“…Another important problem is the driver privacy concerns as there are many people who do not feel comfortable to be exposed to cameras. Some overroadway-based systems address the privacy concerns by adopting different types of sensors such as infrared sensors [26] and laser scanner [31].…”

Section: Taxonomy Of Vehicle Classification Technologiesmentioning

confidence: 99%

“…Another laser scanner-based approach is developed by Chidlovskii et al [31]. The key contribution of this vehicle classification system in comparison with [87] is to utilize the specific domain knowledge, i.e., the vehicle shapes to enhance the classification accuracy.…”

Section: Privacy Preserving Solutionsmentioning

confidence: 99%

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

Won

2020

IEEE Access

148

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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 the 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.

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Vehicle type classification from laser scans with global alignment kernels

Cited by 8 publications

References 17 publications

Clasificación de vehículos basada en bolsa de palabras visuales y usando imágenes de rango

Clasificación de vehículos basada en bolsa de palabras visuales y usando imágenes de rango

Review of IoT Sensor Systems Used for Monitoring the Road Infrastructure

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

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