Description
Wild Life Safety and Monitoring System using Opencv and Raspberry pi
ABSTRACT
The detection of object recognition from images plays a vital role in Computer vision, cognitive science, and Forensic Science. Object recognition is an important biometric object in image and video databases of surveillance systems. Detecting and locating wild animals in an image or image sequence are important tasks in dynamic environments, such as videos, where noise conditions, illuminations, locations of subjects, and pose can vary significantly from frame to frame. An automated system for object recognition in the real-time background for home and wild areas. This paper presents the development of cameras based on object recognition systems and alerting systems that can be used for animal detection. Here multiple wild animals are detected and recognized with the database trained multiple texture-based features. This system can be used for farmlands near wildlife sanctuaries where there is a risk factor of animals intruding on the farmland to feed on the crop. This can be avoided by an early warning system that can detect animals long before entering the farmland.
Wild Life Safety and Monitoring System using Opencv
EXISTING SYSTEM
- Current shocks based system
- Manual control system
PROPOSED SYSTEM
- Camera with object comparison and recognition system.
- We used it as a safety and security system.
- We identify the exact objects like elephant, deer, etc
- Feature Extraction
Wild Life Safety and Monitoring System using Opencv and Raspberry pi
ADVANTAGES OF PROPOSED SYSTEM
- Easy to setup compared to old methods
- Accuracy of output is increased
- User-friendly system
- Cost-effective system
Wild Life Safety and Monitoring System using Opencv and Raspberry pi
ARCHITECTURE DIAGRAM
HARDWARE REQUIREMENTS
- Raspberry Pi
- Power Adapter
- HDMI to VGA converter (optional, when connecting to Monitor)
- PIR sensor
- Camera
- Monitor
SOFTWARE REQUIREMENTS
- Programming Platform: Python IDE
- Programming Language: Python
- Raspbian Stretch OS
- OpenCV library
REFERENCE
[1] X. Wu, D. Hong, J. Chanussot, Y. Xu, R. Tao, and Y. Wang, ‘‘Fourier-based rotation-invariant feature boosting: An efficient framework for geospatial object detection,’’ IEEE Geosci. Remote Sens. Lett., vol. 17, no. 2, pp. 302–306, Feb. 2020.
[2] X. Wu, D. Hong, J. Tian, J. Chanussot, W. Li, and R. Tao, ‘‘ORSIm detector: A novel object detection framework in optical remote sensing imagery using spatial-frequency channel features,’’ IEEE Trans. Geosci. Remote Sens., vol. 57, no. 7, pp. 5146–5158, Jul. 2019.
[3] S. Ren, K. He, R. Girshick, and J. Sun, ‘‘Faster R-CNN: Towards real-time object detection with region proposal networks,’’ IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 6, pp. 1137–1149, Jun. 2017.
[4] Y. Li, S. Li, C. Chen, A. Hao, and H. Qin, ‘‘Accurate and robust video saliency detection via self-paced diffusion,’’ IEEE Trans. Multimedia, vol. 22, no. 5, pp. 1153–1167, May 2020.
[5] K. Kang, W. Ouyang, H. Li, and X. Wang, ‘‘Object detection from video tubeless with convolutional neural networks,’’ in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CPR), Jun. 2016, pp. 817–825.
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