Advanced Video Processing and Data Transmission Technology for Unmanned Ground Vehicles in the Internet of Battlefield Things (loBT)

With the continuous advancement of unmanned technology in various application domains, the development and deployment of blind-spot-free panoramic video systems have gained increasing importance. Such systems are particularly critical in battlefield environments, where advanced panoramic video processing and wireless communication technologies are essential to enable remote control and autonomous operation of unmanned ground vehicles (UGVs). However, conventional video surveillance systems suffer from several limitations, including limited field of view, high processing latency, low reliability, excessive resource consumption, and significant transmission delays. These shortcomings impede the widespread adoption of UGVs in battlefield settings. To overcome these challenges, this paper proposes a novel multi-channel video capture and stitching system designed for real-time video processing. The system integrates the Speeded-Up Robust Features (SURF) algorithm and the Fast Library for Approximate Nearest Neighbors (FLANN) algorithm to execute essential operations such as feature detection, descriptor computation, image matching, homography estimation, and seamless image fusion. The fused panoramic video is then encoded and assembled to produce a seamless output devoid of stitching artifacts and shadows. Furthermore, H.264 video compression is employed to reduce the data size of the video stream without sacrificing visual quality. Using the Real-Time Streaming Protocol (RTSP), the compressed stream is transmitted efficiently, supporting real-time remote monitoring and control of UGVs in dynamic battlefield environments. Experimental results indicate that the proposed system achieves high stability, flexibility, and low latency. With a wireless link latency of 30 ms, the end-to-end video transmission latency remains around 140 ms, enabling smooth video communication. The system can tolerate packet loss rates (PLR) of up to 20% while maintaining usable video quality (with latency around 200 ms). These properties make it well-suited for mobile communication scenarios demanding high real-time video performance.

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Publication Details

Journal
Computers, materials & continua/Computers, materials & continua (Print)
Published
2025-01-01
DOI
https://doi.org/10.32604/cmc.2025.072692
Primary Topic
Video Surveillance and Tracking Methods
Type
article
Field-Weighted Citation Impact
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article

Advanced Video Processing and Data Transmission Technology for Unmanned Ground Vehicles in the Internet of Battlefield Things (loBT)

Tai Liu, Guoyan Zhang, Bo Chen, Mao Ye et al.
Computers, materials & continua/Computers, materials & continua (Print)
Video Surveillance and Tracking Methods
article

Advanced Video Processing and Data Transmission Technology for Unmanned Ground Vehicles in the Internet of Battlefield Things (loBT)

Tai Liu, Guoyan Zhang, Bo Chen, Mao Ye, Feng Wu, Chao Zhu
article en

Abstract

With the continuous advancement of unmanned technology in various application domains, the development and deployment of blind-spot-free panoramic video systems have gained increasing importance. Such systems are particularly critical in battlefield environments, where advanced panoramic video processing and wireless communication technologies are essential to enable remote control and autonomous operation of unmanned ground vehicles (UGVs). However, conventional video surveillance systems suffer from several limitations, including limited field of view, high processing latency, low reliability, excessive resource consumption, and significant transmission delays. These shortcomings impede the widespread adoption of UGVs in battlefield settings. To overcome these challenges, this paper proposes a novel multi-channel video capture and stitching system designed for real-time video processing. The system integrates the Speeded-Up Robust Features (SURF) algorithm and the Fast Library for Approximate Nearest Neighbors (FLANN) algorithm to execute essential operations such as feature detection, descriptor computation, image matching, homography estimation, and seamless image fusion. The fused panoramic video is then encoded and assembled to produce a seamless output devoid of stitching artifacts and shadows. Furthermore, H.264 video compression is employed to reduce the data size of the video stream without sacrificing visual quality. Using the Real-Time Streaming Protocol (RTSP), the compressed stream is transmitted efficiently, supporting real-time remote monitoring and control of UGVs in dynamic battlefield environments. Experimental results indicate that the proposed system achieves high stability, flexibility, and low latency. With a wireless link latency of 30 ms, the end-to-end video transmission latency remains around 140 ms, enabling smooth video communication. The system can tolerate packet loss rates (PLR) of up to 20% while maintaining usable video quality (with latency around 200 ms). These properties make it well-suited for mobile communication scenarios demanding high real-time video performance.

Computers, materials & continua/Computers, materials & continua (Print)Vol. 0(0)
Openalex Percentile: Top 67%
Video Surveillance and Tracking Methods
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