Intelligent Farm Surveillance System for Human Bird Animals Detection

Prabhavati Waghmare, Yogita Dhepale, Sujata Shelke

Abstract


Idea of Intelligent farm surveillance system is to keep watch on farm and motivate lossless farming even the owner is not on field. The traditional farm surveillance requires manual detection of objects. Intelligent farm surveillance system takes us to video level processing techniques to identify the objects from farms video. Many developed countries as well as developing countries are using intelligent farm surveillance system so that they can monitor the farm remotely from anywhere. In this paper we are going to see various techniques used for object detection. Videos are first captured and converted into frames, later this frames are been processed and the actual shape of the object is been detected from a sequence of video frames. For identification of object we are going to use moving object detection, perform a series of morphological operations, feature extraction etc. Main goal of our system is to automatic object detection and notify   the user so that necessary action can be done. We have to make system such that it will reduce false negative rate of detection of suspicious objects in farm.

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References


Technological and Commercial Intelligence Report, Aude-Emmanuelle Fleurant ,CRIM, Technople Defence and Security,April 8, 2009,Intelligent Video Surveillance: Promises and Challenges

Marcus Baum, Florian Faion, and Uwe D. Hanebeck Tracking Ground Moving Extended Objects Using RGBD Data 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), September 13-15, 2012. Hamburg, Germany

Zhang Xiaoyan, Liu LingxiaZhuang Xuchun An Automatic Video Object Segmentation Scheme, Proceedings of 2007 International Symposium on Intelligent Signal Processing and Communication Systems Nov.28-Dec.1, 2007 Xiamen, China.

Wei Huang and Q. M. Jonathan Wu, Detection and Tracking of Multiple Moving Objects in Real-World Scenarios using Attributed Relational Graph, Canadian Conference on Computer and Robot Vision

Hannah M. Dee Sergio A. Velastin, How close are we to solving the problem of automated visual surveillance? A review of real-world surveillance, scientific progress and evaluative mechanisms, Machine Vision and Applications (2008) 19:329343 ,DOI 10.1007/s00138-007- 0077-z

Gua Lihua, A Fast And Automatic Video Object Segmentation Technique, 978-1-4244-2064-3/08/25.00@2008IEEE

Pravin R. Futane, Rajiv V. Dharaskar Video gestures identification and recognition using Fourier descriptor and general fuzzy minmax neural network for subset of Indian sign language Hybrid Intelligent Systems (HIS), 2012 - ieeexplore.ieee.org

Ramesh M. Kagalkar, S.V Gumaste, Gradient Based Key Frame Extraction for Continuous Indian Sign Language Gesture Recognition and Sentence Formation in Kannada Language: A Comparative Study of ClassifiersInternational Journal of Computer Sciences and engineering, Vol 4, Issue 9, 2016

Ramesh M. Kagalkar, S.V Gumaste, Automatic Graph Based Clustering for Image Searching and Retrieval from Database, Software Engineering and Technology, 8 (2), 39-49, 2016




 

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