17.1.4.5.1 Camera Networks for Surveillance

Wang, B.[Bang],
Coverage problems in sensor networks: A survey,
Surveys(43), No. 4, October 2011, pp. xx-yy.
DOI Link 1110
Survey, Sensor Networks. Sensor networks, which consist of sensor nodes each capable of sensing environment and transmitting data, have lots of applications in battlefield surveillance, environmental monitoring, industrial diagnostics, etc. BibRef

Dieber, B., Micheloni, C., Rinner, B.,
Resource-Aware Coverage and Task Assignment in Visual Sensor Networks,
CirSysVideo(21), No. 10, October 2011, pp. 1424-1437.
IEEE DOI 1110
BibRef

Dieber, B., Rinner, B.,
Resource-aware sensor selection and task assignment,
RAWNETS11(438-440).
IEEE DOI 1111
BibRef

Chen, T.C., Chen, T.S., Wu, P.W.,
On Data Collection Using Mobile Robot in Wireless Sensor Networks,
SMC-A(41), No. 6, November 2011, pp. 1213-1224.
IEEE DOI 1110
BibRef

Antonio, P., Grimaccia, F., Mussetta, M.,
Architecture and Methods for Innovative Heterogeneous Wireless Sensor Network Applications,
RS(4), No. 5, May 2012, pp. 1146-1161.
DOI Link 1205
BibRef

Mavrinac, A.[Aaron], Chen, X.[Xiang],
Modeling Coverage in Camera Networks: A Survey,
IJCV(101), No. 1, January 2013, pp. 205-226.
WWW Link. 1302
Survey, Camera Networks. survey of geometric and topological coverage models
See also automatic calibration method for stereo-based 3D distributed smart camera networks, An. BibRef

Ahmadi, H.R., Vosoughi, A.,
Optimal Training and Data Power Allocation in Distributed Detection With Inhomogeneous Sensors,
SPLetters(20), No. 4, April 2013, pp. 339-342.
IEEE DOI 1303
BibRef

Yoon, Y., Kim, Y.H.,
An Efficient Genetic Algorithm for Maximum Coverage Deployment in Wireless Sensor Networks,
Cyber(43), No. 5, 2013, pp. 1473-1483.
IEEE DOI 1309
Genetic algorithm; maximum coverage; sensor deployment; solution space BibRef

Xu, J.[Jiejun], Jagadeesh, V., Ni, Z.F.[Ze-Feng], Sunderrajan, S., Manjunath, B.S.,
Graph-Based Topic-Focused Retrieval in Distributed Camera Network,
MultMed(15), No. 8, December 2013, pp. 2046-2057.
IEEE DOI 1402
edge detection BibRef

Ning, H.S.[Huan-Sheng], Liu, H.[Hong], Du, W.[Wei], Wu, J.J.[Jia-Jia], Wang, Z.[Ziou], Yang, L.T., Min, G.Y.[Ge-Yong],
Human-Attention Inspired Resource Allocation for Heterogeneous Sensors in the Web of Things,
IEEE_Int_Sys(28), No. 6, November 2013, pp. 20-28.
IEEE DOI 1404
Internet of Things BibRef

Abas, K.[Kevin], Porto, C.[Caio], Obraczka, K.[Katia],
Wireless Smart Camera Networks for the Surveillance of Public Spaces,
Computer(47), No. 5, May 2014, pp. 37-44.
IEEE DOI 1407
Bandwidth BibRef

Prati, A.[Andrea], Qureshi, F.Z.[Faisal Z.],
Integrating Consumer Smart Cameras into Camera Networks: Opportunities and Obstacles,
Computer(47), No. 5, May 2014, pp. 45-51.
IEEE DOI 1407
Algorithm design and analysis BibRef

Mitra, K.[Kaushik], Veeraraghavan, A.[Ashok], Sankaranarayanan, A.C.[Aswin C.], Baraniuk, R.G.[Richard G.],
Toward Compressive Camera Networks,
Computer(47), No. 5, May 2014, pp. 52-59.
IEEE DOI 1407
Cameras BibRef

Redondi, A., Buranapanichkit, D., Cesana, M., Tagliasacchi, M., Andreopoulos, Y.,
Energy Consumption of Visual Sensor Networks: Impact of Spatio-Temporal Coverage,
CirSysVideo(24), No. 12, December 2014, pp. 2117-2131.
IEEE DOI 1412
Zigbee BibRef

Chadha, A.[Aaron], Abbas, A.[Alhabib], Andreopoulos, Y.F.[Yi-Fannis],
Video Classification With CNNs: Using the Codec as a Spatio-Temporal Activity Sensor,
CirSysVideo(29), No. 2, February 2019, pp. 475-485.
IEEE DOI 1902
Decoding, Training, Complexity theory, deep learning BibRef

Imran, M., Shahzad, K., Ahmad, N., O'Nils, M., Lawal, N., Oelmann, B.,
Energy-Efficient SRAM FPGA-Based Wireless Vision Sensor Node: SENTIOF-CAM,
CirSysVideo(24), No. 12, December 2014, pp. 2132-2143.
IEEE DOI 1412
Complexity theory BibRef

Roach, J.H., Marks, R.J., Thompson, B.B.,
Recovery from Sensor Failure in an Evolving Multiobjective Swarm,
SMCS(45), No. 1, January 2015, pp. 170-174.
IEEE DOI 1502
evolutionary computation BibRef

Hu, T.[Tao], Messelodi, S.[Stefano], Lanz, O.[Oswald],
Dynamic task decomposition for decentralized object tracking in complex scenes,
CVIU(134), No. 1, 2015, pp. 89-104.
Elsevier DOI 1504
BibRef
Earlier:
Dynamic Task Decomposition for Probabilistic Tracking in Complex Scenes,
ICPR14(4134-4139)
IEEE DOI 1412
Multi-camera tracking. Cameras BibRef

Yaagoubi, R.[Reda], El Yarmani, M.[Mabrouk], Kamel, A.[Abdullah], Khemiri, W.[Walid],
HybVOR: A Voronoi-Based 3D GIS Approach for Camera Surveillance Network Placement,
IJGI(4), No. 2, 2015, pp. 754.
DOI Link 1507
BibRef

Rinner, B.[Bernhard], Esterle, L.[Lukas], Simonjan, J.[Jennifer], Nebehay, G.[Georg], Pflugfelder, R.[Roman], Dominguez, G.F.[Gustavo Fernandez], Lewis, P.R.[Peter R.],
Self-Aware and Self-Expressive Camera Networks,
Computer(48), No. 7, July 2015, pp. 21-28.
IEEE DOI 1506
Cameras BibRef

Li, H.[Huan], Fan, H.[Hong], Li, J.[Jia], Chen, N.C.[Neng-Cheng],
Pull-Based Modeling and Algorithms for Real-Time Provision of High-Frequency Sensor Data from Sensor Observation Services,
IJGI(5), No. 4, 2016, pp. 51.
DOI Link 1604
BibRef

de Farias, C.M.[Claudio M.], Li, W.[Wei], Delicato, F.C.[Flávia C.], Pirmez, L.[Luci], Zomaya, A.Y.[Albert Y.], Pires, P.F.[Paulo F.], de Souza, J.N.[José N.],
A Systematic Review of Shared Sensor Networks,
Surveys(48), No. 4, May 2016, pp. Article No 51.
DOI Link 1605
Survey, Sensor Networks. A systematic literature survey on SSNs. The main goal of the article is to provide the reader with the opportunity to understand what has been done and what remains as open issues in this field, as well as which are the pivotal factors of this evolutionary design and how this kind of design can be exploited by a wide range of WSN applications. BibRef

Piciarelli, C., Esterle, L., Khan, A., Rinner, B., Foresti, G.L.,
Dynamic Reconfiguration in Camera Networks: A Short Survey,
CirSysVideo(26), No. 5, May 2016, pp. 965-977.
IEEE DOI 1605
Cameras BibRef

Liu, J.B.[Jun-Bin], Sridharan, S.[Sridha], Fookes, C.[Clinton],
Recent Advances in Camera Planning for Large Area Surveillance: A Comprehensive Review,
Surveys(49), No. 1, July 2016, pp. Article No 6.
DOI Link 1608
With recent advances in consumer electronics and the increasingly urgent need for public security, camera networks have evolved from their early role of providing simple and static monitoring to current complex systems capable of obtaining extensive video information for intelligent processing, such as target localization, identification, and tracking. In all cases, it is of vital importance that the optimal camera configuration (i.e., optimal location, orientation, etc.) is determined before cameras are deployed as a suboptimal placement solution will adversely affect intelligent video surveillance and video analytic algorithms. The optimal configuration may also provide substantial savings on the total number of cameras required to achieve the same level of utility. In this article, we examine most, if not all, of the recent approaches (post 2000) addressing camera placement in a structured manner. We believe that our work can serve as a first point of entry for readers wishing to start researching into this area or engineers who need to design a camera system in practice. To this end, we attempt to provide a complete study of relevant formulation strategies and brief introductions to most commonly used optimization techniques by researchers in this field. We hope our work to be inspirational to spark new ideas in the field. BibRef

Zarezadeh, A.A.[Ali Akbar], Bobda, C.[Christophe], Yonga, F.[Franck], Mefenza, M.[Michael],
Efficient network clustering for traffic reduction in embedded smart camera networks,
RealTimeIP(12), No. 4, December 2016, pp. 813-826.
Springer DOI 1612
BibRef

Petitti, A., di Paola, D., Milella, A., Lorusso, A., Colella, R., Attolico, G., Caccia, M.,
A Network of Stationary Sensors and Mobile Robots for Distributed Ambient Intelligence,
IEEE_Int_Sys(31), No. 6, November 2016, pp. 28-34.
IEEE DOI 1612
ambient intelligence BibRef

Alsmirat, M.A.[Mohammad A.], Jararweh, Y.[Yaser], Obaidat, I.[Islam], Gupta, B.B.[Brij B.],
Automated wireless video surveillance: an evaluation framework,
RealTimeIP(13), No. 3, September 2017, pp. 527-546.
Springer DOI 1710
BibRef

Wang, M., Cheng, B., Yuen, C.,
Joint Coding-Transmission Optimization for a Video Surveillance System With Multiple Cameras,
MultMed(20), No. 3, March 2018, pp. 620-633.
IEEE DOI 1802
Cameras, Encoding, Internet, Real-time systems, Streaming media, Surveillance, Video coding, High definition, real time, wide viewing angle BibRef

Jiang, Z.W.[Zheng-Wei],
RETRACTED: Camera network analysis for visual surveillance in electric industrial context,
JVCIR(69), 2020, pp. 102839.
Elsevier DOI 2006
BibRef
And: Original: JVCIR(56), 2018, pp. 201-206. 1811
Camera network, Topology, Sparse, Visual surveillance BibRef

Dabrowski, A.[Adam], Matczak, P.[Piotr], Wójtowicz, A.[Andrzej], Leitner, M.[Michael],
Identification of Experimental and Control Areas for CCTV Effectiveness Assessment: The Issue of Spatially Aggregated Data,
IJGI(7), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Tomic, S.[Slavisa], Beko, M.[Marko], Tuba, M.[Milan],
A Linear Estimator for Network Localization Using Integrated RSS and AOA Measurements,
SPLetters(26), No. 3, March 2019, pp. 405-409.
IEEE DOI 1903
computational complexity, cooperative communication, maximum likelihood estimation, wireless sensor networks, weighted least squares (WLS) BibRef

Kapach, Z.[Zohar], Ulmer, A.[Andrew], Merrick, D.[Daniel], Alikhan, A.[Arshad], Lu, Y.H.[Yung-Hsiang], Mohan, A.[Anup], Kaseb, A.S.[Ahmed S.], Thiruvathukal, G.K.[George K.],
Cloud Resource Optimization for Processing Multiple Streams of Visual Data,
MultMedMag(26), No. 3, July 2019, pp. 31-41.
IEEE DOI 1909
Cloud computing, Cameras, Streaming media, Graphics processing units, Real-time systems, Visualization. BibRef

Pellicanò, N.[Nicola], Aldea, E.[Emanuel], Le Hégarat-Mascle, S.[Sylvie],
Wide baseline pose estimation from video with a density-based uncertainty model,
MVA(30), No. 6, September 2019, pp. 1041-1059.
Springer DOI 1909
BibRef

Bollen, E.[Erik], Kuijpers, B.[Bart], Soliani, V.[Valeria], Vaisman, A.[Alejandro],
Temporal Paths in Real-World Sensor Networks,
IJGI(13), No. 2, 2024, pp. 36.
DOI Link 2402
BibRef

Reis, R.O., Dias, I.H., Prates, R., Schwartz, W.R.,
Cycleptz: The Learning-Based Control Method For Master-Slave Camera Systems,
ICIP20(286-290)
IEEE DOI 2011
Cameras, Master-slave, Training, Computational modeling, Neural networks, Surveillance, Control systems, PTZ cameras, video surveillance. BibRef

Callemein, T., van Ranst, W., Goedemé, T.,
The autonomous hidden camera crew,
MVA17(47-50)
DOI Link 1708
Cameras, Detectors, Hardware, Organizations, Streaming media, Switches, TV BibRef