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Research and development of general technical specifications for video surveillance systems in the coal mining industry

Year: 2025
Type: article
Abstract: To enhance the environmental adaptability and reliability of intelligent analysis of video surveillance systems in the coal mining industry under complex modern mine scenarios, in addition to image monitoring functions, the system should include: ① a video image quality self-diagnosis mechanism that automatically detects and diagnoses the quality of video images and the effectiveness of remote control of the PTZ lens. ② a multi-level Intelligent Video Analysis (IVA) system, including target detection, target recognition, behavior recognition, and event detection. ③ a configurable IVA framework that supports selecting and configuring IVA functions according to needs. ④ structured video description and indexing, supporting automatic description of IVA results and the establishment of indexes based on IVA results and related information. ⑤ cross-system alarm linkage, equipped with data interaction interfaces for alarm linkage, sending IVA results and other information to other devices or systems. The main IVA functions of the system should be quantitatively evaluated by indicators such as maximum error, false alarm rate, missed detection rate, and IVA latency, with the maximum false alarm rate not exceeding 5%, missed detection rate not exceeding 10%, and IVA latency not exceeding 2 seconds. Design multi-scenario simulation tests to comprehensively verify system performance, mainly including: ① verification of video image quality self-diagnosis function and system image quality through video quality interference simulation and subjective evaluation mechanisms. ② evaluation of IVA functions by establishing a triple test system including test video import, test video re-shooting, and scenario simulation. ③ verification of system maximum capacity by total bandwidth occupancy calculation or maximum number of video signal access tests.
DOI: 10.13272/j.issn.1671-251x.18245
Authors: CHANG Lin, LI Zongwei
Language: English
Open Access status: green
OpenAlex ID: W6922777254
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