Vehicle type classification from laser scanner profiles: A benchmark of feature descriptors

Sandhawalia, Harsimrat; Rodríguez-Serrano, José A.; Poirier, Hervé; Csurka, Gabriela

doi:10.1109/itsc.2013.6728283

Cited by 20 publications

(19 citation statements)

References 20 publications

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“…Like in the previous evaluation [17], all methods show a similar performance in terms of per-vehicle accuracy. It reflects the biased class distribution observed in the current tolling system installation.…”

Section: B Vehicle Type Classification Resultssupporting

confidence: 69%

“…• Raw: The raw profile features normalized to a 15x15 grid. • FIS: Fisher image signatures as described in [17]. Two sets of global and raw features are available after the pre-processing step.…”

Section: B Vehicle Type Classification Resultsmentioning

confidence: 99%

“…A collection of raw laser scans are pre-processed in order to reduce the noise in the measured signal [17], and to extract individual vehicles to feed the classifier with information from one vehicle at a time. The following sequence of operations was performed: First, individual transactions are isolated.…”

Section: Laser Scans Pre-processingmentioning

confidence: 99%

“…In the previous paper [17], the laser scans were treated as images with the signal depth being represented by different colors. This allowed to deploy a generic approach to the image classification based on extraction of a set of Fisher features.…”

Section: Introductionmentioning

confidence: 99%

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Vehicle type classification from laser scans with global alignment kernels

Chidlovskii

Rodríguez-Serrano

2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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This paper addresses the problem of vehicle classification using laser scanner profiles, which is usually found as a component of electronic tolling systems. Laser scanners obtain a 3D measurement of the vehicle surface. Previous approaches treated the laser scans as images. In addition to high-level features (such as width, height, length and other measurements) from the scanner profiles, feature descriptors have been used for supervised classification of laser scanner profiles. This allowed to deploy a generic approach to the image classification based on Fisher features. In this paper we adopt a different approach which extracts and analyses vehicle shapes from the laser scans. The method proceeds to finding typical vehicle shapes and classifying them through the global shape alignment.

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Section: B Vehicle Type Classification Resultssupporting

confidence: 69%

Section: B Vehicle Type Classification Resultsmentioning

confidence: 99%

Section: Laser Scans Pre-processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Vehicle type classification from laser scans with global alignment kernels

Chidlovskii

Rodríguez-Serrano

2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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“…To address the space shift and scaling problem, the dynamic time warping (DTW) [107] and the global alignment kernel (GA) [108] are used. The same six vehicle types as [87] were used for experiments. The best classification accuracy achieved was 86.8%.…”

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 Classification Based on Convolutional Networks Applied to FMCW Radar Signals

Capobianco

Facheris

Cuccoli

et al. 2018

Advances in Intelligent Systems and Computing

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This paper investigates the processing of Frequency Modulated-Continuos Wave (FM-CW) radar signals for vehicle classification. In the last years deep learning has gained interest in several scientific fields and signal processing is not one exception. In this work we address the recognition of the vehicle category using a Convolutional Neural Network (CNN) applied to range Doppler signature. The developed system first transforms the 1-dimensional signal into a 3-dimensional signal that is subsequently used as input to the CNN. When using the trained model to predict the vehicle category we obtain good performance.

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Vehicle type classification from laser scanner profiles: A benchmark of feature descriptors

Cited by 20 publications

References 20 publications

Vehicle type classification from laser scans with global alignment kernels

Vehicle type classification from laser scans with global alignment kernels

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

Vehicle Classification Based on Convolutional Networks Applied to FMCW Radar Signals

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