Social Distance Monitoring System using OpenCV | Python

Description

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High-resolution satellite images contain a huge amount of information. Shadows in such images generate real problems in classifying and extracting the required information. Although signals recorded in shadow area are weak, it is still possible to recover them. Significant work is already done in shadow detection direction but, classifying shadow pixels from vegetation pixels correctly is still an issue as dark vegetation areas are still misclassified as shadow in some cases. Background subtraction is a commonly used method to detect moving objects from videos captured by static cameras. However, shadows and reflections significantly affect the output of background subtraction algorithms, and distort the shape of the objects obtained as a result. Thus, shadow detection and removal is a crucial post-processing step to perform accurate object tracking required by different applications. We present a lightweight method to detect and remove shadows as well as reflection effects in indoor and outdoor environments by using spatial and spectral features. This method incorporates an adaptive way to set thresholds to avoid preset numbers. We present a comparison of the outputs we obtained with those of several other methods. The experimental results demonstrate the success of the proposed algorithm.

Existing System:

  • Compensation,
  • Principal component
  • Shadow threshold.

Drawbacks

  • In appearance based methods, less accurate of features description because of whole

Image consideration

  • In geometric based methods, the geometric features like distance between eyes, face

length and width, etc., are considered which not provides optimal results

Proposed System:

  • Adaptive Local Filter
  • Contrast Limited Adaptive Histogram Equalization

Applications:

  • Queue forming
  • Identification of shadow

Software Required:

  • Mat lab 7.5 and above versions

REFERENCES:

[1] T. F. Y. Vicente, L. Hou, C.-P. Yu, M. Hoai, and D. Samaras, ?Largescale training of shadow detectors with noisily-annotated shadow examples,? in Computer Vision ? ECCV 2016, B. Leibe, J. Matas, N. Sebe, and M. Welling, Eds. Cham: Springer International Publishing, 2016, pp. 816?832.

[2] J. Wang, X. Li, L. Hui, and J. Yang, ?Stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal,? CoRR, vol. abs/1712.02478, 2017.

[3] J. Zhu, K. G. G. Samuel, S. Z. Masood, and M. F. Tappen, ?Learning to recognize shadows in monochromatic natural images,? in 2010 IEEE Computer Society conf. on Computer Vision and Pattern Recognition, 2010, pp. 223?230.

[4] D. Kersten, D. C. Knill, P. Mamassian, and I. Blthoff, ?Illusory motion from shadows,? Nature, vol. 379, no. 31, 1996.

[5] S. Jiddi, P. Robert, and E. Marchand, ?Estimation of position and intensity of dynamic light sources using cast shadows on textured real surfaces,? in 2018 25th IEEE Int. conf. on Image Proc. (ICIP), 2018, pp. 1063?1067. [6] Y. Zhang and D. Zhu, ?Height retrieval in postprocessing-based VideoSAR image sequence using shadow information,? IEEE Sensors J., vol. 18, no. 19, pp. 8108?8116, 2018.

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