A self-organizing map to improve vehicle detection in flow monitoring systems

Luque‐Baena, Rafael Marcos; López‐Rubio, Ezequiel; Domínguez, Enrique; Palomo, Esteban J.; Jerez, José M.

doi:10.1007/s00500-014-1575-3

Cited by 21 publications

(8 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SOM algorithm is utilized in classification tasks, as shown by Xia et al [ 31 ] with their work on the classification and grouping of lithium cells in a production line. The different variants and learning paradigms of this algorithm are also applicable in satellite-image classification [ 32 , 33 ] and its use in image segmentation for the improvement of object detection in video surveillance [ 34 ].…”

Section: Related Researchmentioning

confidence: 99%

Low-Cost Road-Surface Classification System Based on Self-Organizing Maps

Andrades

Aguilar

García

et al. 2020

Sensors

View full text Add to dashboard Cite

Expanding the performance and autonomous-decision capability of driver-assistance systems is critical in today’s automotive engineering industry to help drivers and reduce accident incidence. It is essential to provide vehicles with the necessary perception systems, but without creating a prohibitively expensive product. In this area, the continuous and precise estimation of a road surface on which a vehicle moves is vital for many systems. This paper proposes a low-cost approach to solve this issue. The developed algorithm resorts to analysis of vibrations generated by the tyre-rolling movement to classify road surfaces, which allows for optimizing vehicular-safety-system performance. The signal is analyzed by means of machine-learning techniques, and the classification and estimation of the surface are carried out with the use of a self-organizing-map (SOM) algorithm. Real recordings of the vibration produced by tyre rolling on six different types of surface were used to generate the model. The efficiency of the proposed model (88.54%) and its speed of execution were compared with those of other classifiers in order to evaluate its performance.

show abstract

Section: Related Researchmentioning

confidence: 99%

Low-Cost Road-Surface Classification System Based on Self-Organizing Maps

Andrades

Aguilar

García

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Most of the cameras located on highways capture the images with perspective, which causes that there is no homogeneity in the distances in each part of the frame. Thus, in order to estimate the real distances in the scenario and the speed of the vehicles, a Self-Organizing Map (SOM) model is considered [14]. A feature vector z ∈ R D is extracted from each detected object where D is the number of chosen features.…”

Section: Speed Estimationmentioning

confidence: 99%

Road Pollution Estimation Using Static Cameras And Neural Networks

Molina‐Cabello

Luque‐Baena

López‐Rubio

et al. 2018

2018 International Joint Conference on Neural Networks (IJCNN)

View full text Add to dashboard Cite

This paper presents a methodology for estimating pollution on roads by analyzing traffic video sequences. The objective is to take advantage of the huge network of static cameras which is possible to find in the road system of any state or country to estimate the pollution on each area. This proposal uses deep learning neural networks for the object detection, and a pollution estimation model based on the frequency of vehicles and their speed. The experiments show promising results which suggest that the system can be used alone or combined with existing systems for measuring pollution on roads.

show abstract

“…In order to learn the details of the scene, a large number of neurons N is employed, which is lower but in the same order of magnitude as the number of pixels of the full panorama. Following the strategy in Luque‐Baena, López‐Rubio, Domínguez, Palomo, and Jerez (), the input vectors

boldx \in R^{5}

are divided into two sections. The first section contains the positional information in the video frame, whereas the second section contains the color features.…”

Section: The Modelsmentioning

confidence: 99%

Panorama construction for PTZ camera surveillance with the neural gas network

Thurnhofer‐Hemsi¹,

López‐Rubio²,

Domínguez³

et al. 2017

Expert Systems

Self Cite

View full text Add to dashboard Cite

The construction of a model of the background of a scene still remains as a challenging task in video surveillance systems, in particular for moving cameras. This work presents a novel approach for constructing a panoramic background model based on the neural gas network and a subsequent piecewise linear interpolation by Delaunay triangulation. Furthermore, an ensemble model of neural gas networks is also proposed. The approach can handle arbitrary camera directions and zooms for a pan‐tilt‐zoom camera‐based surveillance system. After testing the proposed approach on several indoor sequences, the results demonstrate that the proposed methods are effective and suitable to use for real‐time video surveillance applications.

show abstract

A self-organizing map to improve vehicle detection in flow monitoring systems

Cited by 21 publications

References 39 publications

Low-Cost Road-Surface Classification System Based on Self-Organizing Maps

Low-Cost Road-Surface Classification System Based on Self-Organizing Maps

Road Pollution Estimation Using Static Cameras And Neural Networks

Panorama construction for PTZ camera surveillance with the neural gas network

Contact Info

Product

Resources

About