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Quantifying response latency in video surveillance systems using object detection techniques

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Abstract

As video surveillance systems become increasingly essential for railway operations, accurate and precise performance testing is crucial. Traditional methods for response latency testing rely on manual readings with millisecond-level clocks, which can lead to compatibility issues, software crashes, and potential security risks. To address these challenges, this paper proposes a response latency testing method based on object detection for railway video surveillance systems. The response latency test method includes two application scenarios: real-time video call and pan–tilt–zoom camera control response. By leveraging the YOLO-V5 model and object detection techniques, the response speed of railway video surveillance systems is effectively evaluated, ensuring testing precision. Experimental results validate the efficiency and feasibility of the proposed approach, emphasizing its enhanced stability and compatibility compared to traditional methods. The proposed approach offers an innovative solution for testing the response lantency of railway video surveillance systems, contributing to the enhancement and optimization of railway operations.

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Abbreviations

PTZ:

Pan–tilt–zoom

YOLO:

You only look once

NMS:

Non-maximum suppression

ORB:

Oriented FAST and Rotated BRIEF

FAST:

Features from accelerated segment test

BRIEF:

Binary robust independent elementary features

BF:

Brute-force

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Funding

This work was supported Bei**g Natural Science Foundation (L211002).

Author information

Authors and Affiliations

  1. State Key Lab of Advanced Rail Autonomous Operation, Bei**g Jiaotong University, Bei**g, People’s Republic of China

    Jia Miao, Li Zhu, Hongli Zhao & Sen Lin

  2. Communications Signal Research Institute of Railway Research Institute, Bei**g, People’s Republic of China

    **njun Gao

Authors
  1. Jia Miao
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  2. Li Zhu
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  3. Hongli Zhao
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  4. Sen Lin
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  5. **njun Gao
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Contributions

Jia Miao conceptualized the study and contributed to the original manuscript; Li Zhu and Hongli Zhao contributed to the formal analysis of the study and participated in the revision of the original manuscript; and Sen Lin and **njun Gao contributed data as well as analyzed the methodology and validated the results.

Corresponding author

Correspondence to Hongli Zhao.

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Miao, J., Zhu, L., Zhao, H. et al. Quantifying response latency in video surveillance systems using object detection techniques. J Supercomput (2024). https://doi.org/10.1007/s11227-024-06185-8

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  • DOI: https://doi.org/10.1007/s11227-024-06185-8

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