Vehicle detection and classification from acoustic signal using ANN and KNN

George, Jobin; Mary, Leena; Riyas, K S

doi:10.1109/iccc.2013.6731694

Cited by 64 publications

(46 citation statements)

References 5 publications

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“…Like the in-roadwaybased systems, different types of sensors have been utilized. Some of the most widely used sensors include magnetometers [21] [22], accelerometers [23], and acoustic sensors [24]. Recently, advanced sensors such as Laser Infrared Detection and Ranging (LIDAR) [25] [4], infrared sensors [26], and Wi-Fi transceivers [27] have been employed.…”

Section: Taxonomy Of Vehicle Classification Technologiesmentioning

confidence: 99%

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

Won

2020

IEEE Access

151

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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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Section: Taxonomy Of Vehicle Classification Technologiesmentioning

confidence: 99%

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

Won

2020

IEEE Access

151

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“…A key challenge is the removal of undesired noise, which is omnipresent in the considered traffic scenario and requires multiple preprocessing and filtering steps. However, the achievable accuracy of solely acoustics-based systems is relatively low (e.g., 73.42% in [28]). Audiovisual cues aim to compensate the shortcomings of the individual systems by combining the low computation complexity of audio systems for vehicle detection with the high classification accuracy of camera systems.…”

Section: B Vehicle Detection and Classification Systemsmentioning

confidence: 99%

The Channel as a Traffic Sensor: Vehicle Detection and Classification Based on Radio Fingerprinting

Sliwa

Piatkowski

Wietfeld

2020

IEEE Internet Things J.

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Ubiquitously deployed Internet of Things (IoT)based automatic vehicle classification systems will catalyze datadriven 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.

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“…The accuracy of supervised extraction methods rely on the features used and the labeled samples. Supervised classification methods mainly include AAN (Artificial Neural Network) [25,26], SVM (support Vector Machine) [27], MRF (Markov Random Field) [28], and ML (Machine Learning) [29]. Many ANN models, such as BP neural network [25], fuzzy neural network, spiking neural network, and hybrid neural network [26,30], have been used for road extraction from remote sensing images.…”

Section: Road Extractionmentioning

confidence: 99%

Road Extraction from Very High Resolution Images Using Weakly labeled OpenStreetMap Centerline

Chen

et al. 2019

IJGI

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Road networks play a significant role in modern city management. It is necessary to continually extract current road structure, as it changes rapidly with the development of the city. Due to the success of semantic segmentation based on deep learning in the application of computer vision, extracting road networks from VHR (Very High Resolution) imagery becomes a method of updating geographic databases. The major shortcoming of deep learning methods for road networks extraction is that they need a massive amount of high quality pixel-wise training datasets, which is hard to obtain. Meanwhile, a large amount of different types of VGI (volunteer geographic information) data including road centerline has been accumulated in the past few decades. However, most road centerlines in VGI data lack precise width information and, therefore, cannot be directly applied to conventional supervised deep learning models. In this paper, we propose a novel weakly supervised method to extract road networks from VHR images using only the OSM (OpenStreetMap) road centerline as training data instead of high quality pixel-wise road width label. Large amounts of paired Google Earth images and OSM data are used to validate the approach. The results show that the proposed method can extract road networks from the VHR images both accurately and effectively without using pixel-wise road training data. scribble annotation for road extraction. The ground truth of the pixel-level annotation in Figure 1b should label every pixel, which is difficult to generate. Figure 1c represents the scribble annotation, which can be easily obtained from OSM. Because the full annotation dataset is expensive to obtain and the scribble annotation is easy to generate, the study of road networks extraction using scribble labels is of great importance.Recently, weakly supervised learning was popular in image segmentation [13][14][15][16]. In these methods, scribbles [17,18], bounding boxes [19,20], clicks [19], and image-level tags [18] are used as supervision for image segmentation. In this work, the OSM centerline is used as a typical scribbles supervision for road extraction.In order to improve annotation efficiency and road extraction performance for automated VHR (Very High Resolution, spatial resolution 0∼2 m/pixel) images interpretation, this paper proposes a weakly supervised method to extract the road network supervised only by the scribble annotation OSM centerline. In this method, graph cut theory and a deep learning technique named Multi-Dilated-ResUNet (MD-ResUNet) are used to make efficient roads extraction.The main contributions of this paper are as follows:1. A novel deep learning approach based on revised ResUNet with hybrid loss is proposed for road extraction, which can only be supervised by weakly labeled OSM centerline instead of carefully notated pixel-wise road width information. 2. In order to improve the performance of the proposed model furtherly, a novel multi-dilation network with learnable parameters is added to conventional ResUnet. Th...

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Vehicle detection and classification from acoustic signal using ANN and KNN

Cited by 64 publications

References 5 publications

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

The Channel as a Traffic Sensor: Vehicle Detection and Classification Based on Radio Fingerprinting

Road Extraction from Very High Resolution Images Using Weakly labeled OpenStreetMap Centerline

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