Literature DB >> 35591289

Perimeter Intrusion Detection by Video Surveillance: A Survey.

Devashish Lohani1,2, Carlos Crispim-Junior1, Quentin Barthélemy2, Sarah Bertrand2, Lionel Robinault1,2, Laure Tougne Rodet1.   

Abstract

In recent times, we have seen a massive rise in vision-based applications, such as video anomaly detection, motion detection, object tracking, people counting, etc. Most of these tasks are well defined, with a clear idea of the goal, along with proper datasets and evaluation procedures. However, perimeter intrusion detection (PID), which is one of the major tasks in visual surveillance, still needs to be formally defined. A perimeter intrusion detection system (PIDS) aims to detect the presence of an unauthorized object in a protected outdoor site during a certain time. Existing works vaguely define a PIDS, and this has a direct impact on the evaluation of methods. In this paper, we mathematically define it. We review the existing methods, datasets and evaluation protocols based on this definition. Furthermore, we provide a suitable evaluation protocol for real-life application. Finally, we evaluate the existing systems on available datasets using different evaluation schemes and metrics.

Entities:  

Keywords:  i-LIDS; outdoor environment; perimeter intrusion detection; real-time analysis; video surveillance

Mesh:

Year:  2022        PMID: 35591289      PMCID: PMC9104546          DOI: 10.3390/s22093601

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.847


  9 in total

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Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2019-01-15       Impact factor: 3.802

7.  New Generation Deep Learning for Video Object Detection: A Survey.

Authors:  Licheng Jiao; Ruohan Zhang; Fang Liu; Shuyuan Yang; Biao Hou; Lingling Li; Xu Tang
Journal:  IEEE Trans Neural Netw Learn Syst       Date:  2022-08-03       Impact factor: 14.255

8.  The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets.

Authors:  Takaya Saito; Marc Rehmsmeier
Journal:  PLoS One       Date:  2015-03-04       Impact factor: 3.240

9.  Abandoned Object Detection in Video-Surveillance: Survey and Comparison.

Authors:  Elena Luna; Juan Carlos San Miguel; Diego Ortego; José María Martínez
Journal:  Sensors (Basel)       Date:  2018-12-05       Impact factor: 3.576

  9 in total

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