Trackerbot: A robotic surveillance system based on stereo-vision and artificial neural networks

Khosravi, Hooman; Fazl-Ersi, Ehsan

doi:10.1109/icrom.2016.7886781

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Their system used two High-speed camera to calibrated. Khosravi and Fazl-Ersi (Khosravi and Fazl-Ersi, 2017) researched the neural network function in a feed-forward network. Their system was consist of a central PTZ camera and two wide camera, they used the wide angle cameras as its input, and computed the desired pan and tilt values.…”

Section: Related Workmentioning

confidence: 99%

DNN Based Cooperative Calibration for Master-Slave Multi-Camera Network

Hua

Zhai

Liu

2019

Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications

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Master-slave camera systems, consisting of wide-field and pan-tilt-zoom (PTZ) camera, are widely applied in surveillance. They can monitoring the wide scene, and the high-resolution details of interesting target can be captured by PTZ camera. In order to achieve this function, the accurate cooperative calibration for these system is a prerequisite. However, the nonlinear changing PTZ parameters (e.g. intrinsic and extrinsic) with pan, tilt and zoom lead to inaccurate calibration by existing methods. What's more, the process of traditional step-by-step calibration method makes accumulative error. In this paper, we provide a new end-to-end deep neural network for cooperative calibration. This network establishes a mapping relationship between pix coordinate in wide-field camera and control parameters of the PTZ camera. By this model, the control parameters of the PTZ camera can be acquired without any complex camera calibration operation. Experiments show that the proposed neural network has little calibration errors as compared to the ground truth.

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Section: Related Workmentioning

confidence: 99%

DNN Based Cooperative Calibration for Master-Slave Multi-Camera Network

Hua

Zhai

Liu

2019

Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications

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“…Despite their effectiveness, these methods have some disadvantages, such as complex structures and the challenge of deployment for specific expensive sensors or devices. The rapid development of intelligent vision technology and visual sensors, such as monocular cameras [14], [15], stereo cameras [16], [17], and RGB-D sensors [18], [19], has become the core part of the UAV surveillance and recycling system [20], [21]. Martínez et al [22] design a trinocular system, which is composed of three FireWire cameras fixed on the ground, to estimate the vehicle position and orientation by tracking the markers on the UAV.…”

Section: Introductionmentioning

confidence: 99%

Panoramic UAV Surveillance and Recycling System Based on Structure-Free Camera Array

Yang

Zhang

et al. 2019

IEEE Access

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In recent years, unmanned aerial vehicles (UAVs) have rapidly developed, but the illegal use of UAVs by civilians has resulted in disorder and security risks and has increasingly triggered the community concern and worry. Therefore, the monitoring and recycling of UAVs in key regions are of great significance. This paper presents novel panoramic UAV surveillance and autonomous recycling system that is based on the unique structure-free fisheye camera array and has the capability of real-time UAV detection, 3D localization, tracking, and recycling capacity over a very wide field of view. The main characteristics of this paper include the following: 1) constructing a structure-free camera-array-based panoramic UAV surveillance and recycling system; 2) designing a robust dynamic near-infrared laser-source-based self-calibration algorithm for large-scale arbitrary layout of the camera array; and 3) presenting a set of UAV detection, 3D localization, tracking, and autonomous recycling algorithms based on a fish-eye camera array. The system has been tested in various challenging scenarios, including multiple UAVs with significant appearance and scale changes and even poor weather conditions. The extensive experimental results analyzed both qualitatively and quantitatively, and the analysis of the time performance demonstrate the robustness and effectiveness of the proposed system. In addition, we successfully conduct a recycling and landing experiment with the Parrot Beobop. INDEX TERMS UAV, structure-free fisheye camera array, surveillance and recycling system.

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Anti-UAVs Surveillance System based on Ground Random Fisheye Camera Array

Zhi

Yang

et al. 2018

Proceedings of the 2018 International Conference on Image and Graphics Processing

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Trackerbot: A robotic surveillance system based on stereo-vision and artificial neural networks

Cited by 3 publications

References 12 publications

DNN Based Cooperative Calibration for Master-Slave Multi-Camera Network

DNN Based Cooperative Calibration for Master-Slave Multi-Camera Network

Panoramic UAV Surveillance and Recycling System Based on Structure-Free Camera Array

Anti-UAVs Surveillance System based on Ground Random Fisheye Camera Array

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