Integrated radar-camera security system: experimental results

Życzkowski, M.; Pałka, Norbert; Trzciński, Tomasz Piotr; Dulski, R.; Kastek, M.; Trzaskawka, P.

doi:10.1117/12.883775

Cited by 8 publications

(7 citation statements)

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“…The target detection algorithm is mainly based on the characteristics of water surface background or the method based on deep learning. However, these schemes still have the following shortcomings: (1) Most of the methods are not applied to USV, only the data collected by USV are used for simulation experiments [14][15][16][17][18]; (2) The existing methods have the problem of incompatibility between real-time and robustness, which affects the stability of USV environment perception performance; (3) The environmental perception system needs to have good signal transmission with the control system, power system, and communication system [19][20][21][22][23].…”

Section: Related Workmentioning

confidence: 99%

Research on Unmanned Surface Vehicles Environment Perception Based on the Fusion of Vision and Lidar

et al. 2021

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The research of unmanned surface vehicles (usually called USV) is becoming a research hotspot, and real-time and accurate environmental perception is the key technology for achieving autonomous navigation and completing tasks of USV. In this paper, aiming at the technical problems of environmental perception of the USV, from the perspective of sensor multi-information fusion, we build a complete USV perception system (hardware platform and software system). This paper mainly studies objects on the water detection and obstacle avoidance methods based on the information fusion of lidar and vision. In addition, we also studies the method based on morphology to process the reflection of the objects on the water, and the sea-sky detection based on support vector machine (SVM) to assist the objects on the water detection. Through the construction of the USV environment perception system, improving the ability of USV environment perception, and ensuring the safety of autonomous navigation of USV. Our proposed methods have been tested by simulation experiments and actual marine experiment as well as the Hawaii Unmanned Surface Vehicles Challenge, which proves the practicability and stability of our method in the actual environment.

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Section: Related Workmentioning

confidence: 99%

Research on Unmanned Surface Vehicles Environment Perception Based on the Fusion of Vision and Lidar

et al. 2021

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“…The perimeter line (fence) is remotely monitored and additionally the inside area is also under surveillance, including the restricted areas. Detailed requirements for radar-camera system are rather complex [10][11][12][13], but basic requirements are as follows:…”

Section: Concept Of Harbour Security Systemmentioning

confidence: 99%

“…They have ranges from 200m to 3 km and work at the frequencies from 9 GHz to 77 GHz, (millimetre radars). The signal processing hardware should have network interface to transmit radar data to target tracking system [12,13]. Software layer of a tracking system should provide:…”

Section: Concept Of Harbour Security Systemmentioning

confidence: 99%

Multisensor system for the protection of a critical harbour infrastructure

Kastek

Dulski

Życzkowski

et al. 2013

Safety and Security Engineering V

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The critical facilities within a harbour area require special protection, including security systems capable of monitoring both surface and underwater areas. The paper presents the concept of a multisensor security system for harbour protection, capable of complex monitoring of selected critical resources. The proposed system consists of a command centre and several different sensors deployed in key areas, providing effective protection from penetration from land and sea, with special attention focused on the monitoring of the underwater zone. The initial project of such systems was presented, its configuration and initial tests of the selected components. The protection of a surface area is based on medium-range radar, LLTV, and infrared cameras. The protection of an underwater zone is based on sonar, acoustic, and magnetic barriers. Both systems are combined into one, integrated multisensor monitoring system. Some results of theoretical analyses based on the detection of a fast and small surface were presented.

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“…Recognition of a person's type of movement has implications for many aspects of daily life, from security applications to monitoring for assisted living. Discriminating whether a person is running or walking normally in airports or shopping centers, for example, may help video surveillance to detect possible dangerous situations [1][2][3]. Tools designed for this purpose involve the use of contactless devices, and radar technology is particularly suitable for the mentioned scenario.…”

Section: Introductionmentioning

confidence: 99%

People Walking Classification Using Automotive Radar

et al. 2020

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Automotive radars are able to guarantee high performances at the expenses of a relatively low cost, and recently their application has been extended to several fields in addition to the original one. In this paper we consider the use of this kind of radars to discriminate different types of people’s movements in a real context. To this end, we exploit two different maps obtained from radar, that is, a spectrogram and a range-Doppler map. Through the application of dimensionality reduction methods, such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) algorithm, and the use of machine learning techniques we prove that is possible to classify with a very good precision people’s way of walking even employing commercial devices specifically designed for other purposes.

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Integrated radar-camera security system: experimental results

Cited by 8 publications

References 0 publications

Research on Unmanned Surface Vehicles Environment Perception Based on the Fusion of Vision and Lidar

Research on Unmanned Surface Vehicles Environment Perception Based on the Fusion of Vision and Lidar

Multisensor system for the protection of a critical harbour infrastructure

People Walking Classification Using Automotive Radar

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