Evidence map›Paper›PMID 28771167›Full record

ArticleSensors (Basel, Switzerland)2017

Barrier Coverage for 3D Camera Sensor Networks.

Pengju Si, Chengdong Wu, Yunzhou Zhang, Zixi Jia, Peng Ji, Hao Chu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Pengju SiCollege of Information Science and Engineering, Northeastern University, Shenyang 110819, China. sipengju@gmail.com.
Chengdong WuFaculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China. wuchengdong@ise.neu.edu.cn.
Yunzhou ZhangFaculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China. zhangyunzhou@ise.neu.edu.cn.
Zixi JiaFaculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China. jiazixi@ise.neu.edu.cn.
Peng JiFaculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China. jipeng@ise.neu.edu.cn.
Hao ChuFaculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China. chuhao@ise.neu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Barrier coverage, an important research area with respect to camera sensor networks, consists of a number of camera sensors to detect intruders that pass through the barrier area. Existing works on barrier coverage such as local face-view barrier coverage and full-view barrier coverage typically assume that each intruder is considered as a point. However, the crucial feature (e.g., size) of the intruder should be taken into account in the real-world applications. In this paper, we propose a realistic resolution criterion based on a three-dimensional (3D) sensing model of a camera sensor for capturing the intruder's face. Based on the new resolution criterion, we study the barrier coverage of a feasible deployment strategy in camera sensor networks. Performance results demonstrate that our barrier coverage with more practical considerations is capable of providing a desirable surveillance level. Moreover, compared with local face-view barrier coverage and full-view barrier coverage, our barrier coverage is more reasonable and closer to reality. To the best of our knowledge, our work is the first to propose barrier coverage for 3D camera sensor networks.

Indexed as

3D sensing modelbarrier coveragecamera sensor networksresolution

Identifiers

PMID28771167
PMCPMC5579716

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.