Wildlife photography using Raspberry Pi and Open CV

Description

Wildlife photography using Raspberry Pi and Open CV

Wildlife photography is very much difficult to work to stay in the location for more than a while to find the animals and to capture the image at the close. This is a simple Raspberry Pi beginner project which we implemented with the camera, already miniature camera is placed in the forest to capture the unnoticed action of living. But by interfacing this technology with the camera, Images can be captured only if any motion is detected. Raspberry Pi takes video with motion-based technology for storage optimization, with the action-based camera to capture the image.Wildlife photography using Raspberry Pi and Open CV


Wildlife photography using Raspberry Pi and Open CV

Introduction:

Wildlife monitoring plays a key role in an extensive range of medical activities and societal pursuits. Understanding animal conduct and activity patterns are useful for comparing biodiversity and modifications in habitats and land use, heading off dangerous human flora and fauna encounters and damaging habitat overlap, monitoring species health and populace dynamics, and presenting human beings with excessive effect educational stories. Advancements in technology innovations primarily focus on monitoring and controlling different activities. There is increasingly a demand to reach human needs. Most of this technology is mainly focused on efficient monitoring and controlling different activities. An efficient surveillance system is required to monitor and maintain the security of wild animals in zoological parks, and for monitoring their enclosures. Recently many incidents that occur in zoo parks like animals escaping from cages and causing damage to other animals and humans in the zoo, and also sometimes humans also fall into the enclosures of animals and put their life to threat. Some of the cases are shown below.


Existing System

  • Uses normal camera
  • Doesn’t have any automatic system
  • High cost
  • The human presence is compulsory

PROPOSED SYSTEM

  • Uses raspberry pi for capturing wildlife photo
  • Fully automatic
  • Low cost

Advantages

  • It can work in hazardous environments.
  • It is fully automated
  • It can be programmed according to varying needs.

Wildlife photography using Raspberry Pi and Open CV

Block diagram

Wildlife photography using Raspberry Pi and Open CV

CIRCUIT DIAGRAM

Wildlife photography using Raspberry Pi and Open CV
Block diagram explanation

The camera is connected to the Raspberry Pi through USB/Pi camera interfaces. The monitor is connected to the Raspberry Pi using a VGA to HDMI converter.


Hardware tools

  • Raspberry Pi
  • SD card
  • Monitor

SOFTWARE TOOLS

  • Python
  • Library: OpenCV

Wildlife photography using Raspberry Pi and Open CV

References:

[1] ?Image-Based Lateral Position, Steering Behavior Estimation, and Road Curvature Prediction for Motorcycles? in IEEE Robotics and Automation Letters (Volume: 3, Issue: 3, July 2018 )

[2] I. William Schneider, P. Savolainen, D. Moore, “Effects of horizontal curvature on single-vehicle motorcycle crashes along rural two-lane highways”, Transp. Res. Rec. J. Transp. Res. Board, vol. 2194, pp. 91-98, 2010.

[3] M. E.-H. Dabladji, D. Ichalal, H. Arioui, S. Mammar, L. Fridman, “Estimation of lateral dynamics and road curvature for two-wheeled vehicles: A HOSM observer approach”, World Congr. Int. Fed. Autom. Control, vol. 47, no. 3, pp. 2806-2811, Aug. 2014.

[4] Duda, R. O., & Hart, P. E. (1972). Use of the Hough transformation to? detect lines and curves in pictures. Communications of the ACM, 15(1),? 11-15.

[5] Wang, Y., Shen, D., & Teoh, E. K. (2000). Lane detection using spline? model. Pattern Recognition Letters, 21(8), 677-689.

[6] Borkar, A., Hayes, M., & Smith, M. T. (2009, November). Robust lane? detection and tracking with RANSAC and Kalman filter. In 2009 16th IEEE? International Conference on Image Processing (ICIP) (pp. 3261- 3264). IEEE.

[7] Horak, K., & Zalud, L. (2016). Image processing on raspberry pi for mobile robotics. International Journal of Signal Processing? Systems, 4(2), 1-5.


 

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