Smart motion detection sensor based on video processing using self-organizing maps

Ortega-Zamorano, Francisco; Molina‐Cabello, Miguel A.; López‐Rubio, Ezequiel; Palomo, Esteban J.

doi:10.1016/j.eswa.2016.08.010

Cited by 17 publications

(7 citation statements)

References 49 publications

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“…In addition, a higher energy source is required compared to passive sensors. Also, as the desired range of illumination increases, astronomical costs are incurred, which leads to operational efficiency problems [2,3]. A digital surveillance system using an unmanned security system or CCTV to improve the vulnerability of an analog surveillance system is advantageous in that it is highly efficient and free from restrictions related to the installation environment, as compared to existing surveillance systems using a single analog sensor.…”

Section: Introductionmentioning

confidence: 99%

An online graph-based anomalous change detection strategy for unsupervised video surveillance

Kim

Cho

2019

J Image Video Proc.

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Due to various accidents and crime threats to an unspecified number of people, many surveillance technologies have been studied as an interest in individual security continues to increase throughout society. In particular, intelligent video surveillance technology is one of the most active research areas in the field of surveillance; this popularity has been spurred by recent advances in computer vision/image processing and machine learning. The main goal is to automatically detect, recognize, and analyze objects of interest from collected sensor information and then efficiently extract/utilize this useful information, such as by detecting abnormal events or intruders and recognizing objects. Anomalous event detection is a key component of security, and many existing anomaly detection algorithms rely on a foreground subtraction process to detect changes in the foreground scene. By comparing input image frames with a reference image, changed areas of the image can be efficiently detected. However, this technique can be insensitive to static changes and has difficulties in noisy environments since it depends on a reference image. We propose a new strategy for improved dynamic/static change detection that complements the weak points of existing detection methods, which have low robustness in noisy environments. To achieve this goal, we employed a self-organizing map (SOM) for data clustering and regarded the cluster distribution of neurons, represented by the weight of the optimized SOM, as a directed graph problem. We then applied the shortest path algorithm to recognize anomalous events. The real-time monitoring capability of the proposed change detection system was verified by applying it to self-produced test data and the CDnet-2014 dataset. This system showed robustness against noise that was superior to other surveillance systems in various environments.

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Section: Introductionmentioning

confidence: 99%

An online graph-based anomalous change detection strategy for unsupervised video surveillance

Kim

Cho

2019

J Image Video Proc.

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“…Although this type of schemes has mostly been developed in projects dependent on high-performance computing equipment, they have also been implemented in various embedded devices such as Arduino and FPGA [26,27]. An attractive proposal to be able to implement neural algorithms in embedded systems of low processing capacity is the C-Mantec algorithm, proposed in [28] and later implemented in an Arduino UNO board to analyse its characteristics [29].…”

Section: Neural Networkmentioning

confidence: 99%

Execution and analysis of classic neural network algorithms when they are implemented in embedded systems

2019

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Many algorithms related to neural networks are used in a large number of applications, most of them implemented on computational equipment that have great processing and storage capacities, however, new communication schemes such as the Internet of Things, need that neural algorithms can be executed from small electronic devices, devices that do not have large storage or processing capacities, but they can function as intelligent control centres for the different "things" connected to the Internet. Currently, there are various electronic devices that allow generating low-cost intelligent technology projects that permit interaction within the Internet of things, such as the Arduino UNO, Tiva-C, and BeagleBone development boards. In this project, we present the analysis of the Perceptron, ADALINE and Hopfield neural network algorithms, when they are executed within the three mentioned development boards, in order to define the best tool to be utilized when using such neural schemes and few data are processed. Economic cost, temporary response and technical capabilities of electronic devices have been evaluated.

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“…Intrusion detection technologies have been widely used in safety protection of critical infrastructures such as banks, airports, nuclear power plants, military bases and other sensitive sites which require a very high level of security. Common intrusion detection technologies include laser scanners [1], [2], infrared sensors [3], [4], video mobile detectors (VMDs) [5], [6] and electronic fences [7], [8]. However, these technologies have the following defects: (1) Detection equipments such as electronic fences are usually exposed to the outside and vulnerable to intentional evasion and malicious destruction, so they have poor concealments.…”

Section: Introductionmentioning

confidence: 99%

A LCX-Based Intrusion-Detection Sensor Using a Broadband Noise Signal

Xie

Zhang

et al. 2019

IEEE Access

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An intrusion detection sensor system based on a broadband noise signal and two leaky coaxial cables (LCXs) is proposed and experimentally demonstrated. In the proposed sensor, the broadband noise signal is generated by our designed white noise generator used as the detection signal. The detection signal is transmitted and received by two parallel LCXs, forming an invisible electromagnetic monitoring field around the protected area. The intrusion distance along the LCXs can be accurately extracted by the correlation method and the difference between pre-and post-intrusion correlation curves. Experimental results demonstrate the proposed sensor can monitor the intrusion process and locate multiple intruders. The range resolution of 30 cm and the dynamic range of 30 dB can be achieved. Furthermore, the proposed sensor has the strong anti-radio frequency interference (anti-RFI) capability, which makes it very suitable in the complex RF environment. INDEX TERMS Intrusion detection sensor, noise signal, leaky coaxial cable (LCX).

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Smart motion detection sensor based on video processing using self-organizing maps

Cited by 17 publications

References 49 publications

An online graph-based anomalous change detection strategy for unsupervised video surveillance

An online graph-based anomalous change detection strategy for unsupervised video surveillance

Execution and analysis of classic neural network algorithms when they are implemented in embedded systems

A LCX-Based Intrusion-Detection Sensor Using a Broadband Noise Signal

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