CFP last date
20 June 2024
Call for Paper
July Edition
IJCA solicits high quality original research papers for the upcoming July edition of the journal. The last date of research paper submission is 20 June 2024

Submit your paper
Know more
Reseach Article

Speed Detection using IOT

by Rajalakshmi, G. Aravindh, A. Kowshik
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 32
Year of Publication: 2020
Authors: Rajalakshmi, G. Aravindh, A. Kowshik
10.5120/ijca2020920328

Rajalakshmi, G. Aravindh, A. Kowshik . Speed Detection using IOT. International Journal of Computer Applications. 176, 32 ( Jun 2020), 10-13. DOI=10.5120/ijca2020920328

@article{ 10.5120/ijca2020920328,
author = { Rajalakshmi, G. Aravindh, A. Kowshik },
title = { Speed Detection using IOT },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2020 },
volume = { 176 },
number = { 32 },
month = { Jun },
year = { 2020 },
issn = { 0975-8887 },
pages = { 10-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number32/31406-2020920328/ },
doi = { 10.5120/ijca2020920328 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:44:01.455451+05:30
%A Rajalakshmi
%A G. Aravindh
%A A. Kowshik
%T Speed Detection using IOT
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 32
%P 10-13
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Road accidents have been very common in the present world with the prime cause being the careless driving.The necessity to check this has been very `essential and different methods have been used for. However with the advancement in the technology, different governing bodies are demanding some sort of computerized technology to control this problem of over speed driving. At this scenario, we are proposing a system to detect the vehicle which are being driven above the given maximum speed limit that the respective roads or highway lights.

References
  1. R. Pelapur, S. Candemir, F. Bunyak, M. Poostchi, G. Seetharaman, and K. Palaniappan, “Persistent target tracking using likelihood fusion in wide-area and full motion video sequences,” in Proc. 15th Int. Conf. Inf. Fusion (FUSION), Jul. 2012, pp. 2420–2427.
  2. J. Portmann, S. Lynen, M. Chli, and R. Siegwart, “People detection and tracking from aerial thermal views,” in Proc. IEEE Int. Conf. Robot. Automat. (ICRA), May/Jun. 2014, pp. 1794–1800.
  3. M. Danelljan, G. Bhat, F. S. Khan, and M. Felsberg. (2016). “ECO:Efficient convolution operators for tracking.” [Online]. Available: https:// arxiv.org/abs/1611.09224
  4. B. Uzkent, M. J. Hoffman, and A. Vodacek, “Efficient integration of spectral features for vehicle tracking utilizing an adaptive sensor,” Proc. SPIE, vol. 9407, p. 940707, Mar. 2015.
  5. B. Uzkent, Real-Time Aerial Vehicle Detection and Tracking Using a Multi-Modal Optical Sensor. Rochester, NY, USA: Rochester Institute Technology, 2016.
  6. B. Uzkent, A. Rangnekar, and M. J. Hoffman, “Aerial vehicle tracking by adaptive fusion of hyperspectral likelihood maps,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshops (CVPRW), Jul. 2017, pp. 233–242.
  7. AFRL. (2009). Wright-Patterson Air Force Basevvi (WPAFB) Dataset. [Online]. Available: https://www.sdms.afrl.af.mil/index.php?collection= wpafb2009
  8. AFRL. (2007). WAMI Columbus Large Image Format (CLIF) Dataset.
  9. [Online].Available:https://www.sdms.afrl.af.mil/index.php?collection= clif2007
  10. O. Russakovsky et al., “ImageNet large scale visual recognition challenge,” Int. J. Comput. Vis., vol. 115, no. 3, pp. 211–252, Dec. 2015.
  11. B. Uzkent, M. J. Hoffman, A. Vodacek, J. P. Kerekes, and B. Chen, “Feature matching and adaptive prediction models in an object tracking DDDAS,” Procedia Comput. Sci., vol. 18, pp. 1939–1948, Jan. 2013.
  12. B. Uzkent, M. J. Hoffman, A. Vodacek, and B. Chen, “Feature matching with an adaptive optical sensor in a ground target tracking system,” IEEE Sensors J., vol. 15, no. 1, pp. 510–519, Jan. 2015.
  13. R. D. Meyer, K. J. Kearney, Z. Ninkov, C. T. Cotton, P. Hammond, and B. D. Statt, “RITMOS: A micromirror-based multi-object spectrometer,” Proc. SPIE, vol. 5492, pp. 200–219, Sep. 2004.
  14. B. Uzkent, M. J. Hoffman, and A. Vodacek, “Integrating hyperspectral likelihoods in a multidimensional assignment algorithm for aerial vehicle tracking,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 9, no. 9, pp. 4325–4333, Sep. 2016.
  15. S. Han et al., “Efficient generation of image chips for training deep learning algorithms,” Proc. SPIE, vol. 10202, p. 1020203, May 2017.
  16. S. Han and J. P. Kerekes, “Overview of passive optical multispectral and hyperspectral image simulation techniques,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 10, no. 11, pp. 4794–4804, Nov. 2017.
  17. B. Uzkent, M. J. Hoffman, and A. Vodacek, “Spectral validation of measurements in a vehicle tracking DDDAS,” Procedia Comput. Sci., vol. 51, pp. 2493–2502, Jan. 2015.
  18. B. Uzkent, Real-Time Aerial Vehicle Detection and Tracking Using a Multi-Modal Optical Sensor. Rochester, NY, USA: Rochester Institute Technology, 2016.
  19. J. F. Henriques, R. Caseiro, P. Martins, and J. Batista, “High-speed tracking with kernelized correlation filters,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 3, pp. 583–596, Mar. 2015.
  20. S. Hare et al., “Struck: Structured output tracking with kernels,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 10, pp. 2096–2109, Oct. 2016.
Index Terms

Computer Science
Information Sciences

Keywords

Object tracking Centroid Tracking MobileNet SSD.