Abstract
This paper proposed a novel system for non- invasive method of animal tracking and classification in a designated area. The system is based on intelligent de- vices with cameras, which are situated in a designated area, and a main computing unit (MCU) acting as a system master. Intelligent devices track animals and then send data to MCU for evaluation. The main purpose of this system is detection and classification of moving animals in a designated area and then creation of migration corridors of wild animals. In the intelligent devices, background sub- traction method and CAMShift algorithm are used to detect and track animals in the scene. Then, visual descriptors are used to create representation of unknown objects. In order to achieve the best accuracy in classification, key frame extraction method is used to filtrate an object from detection module. Afterwards, Support Vector Machine is used to classify unknown moving animals.
Highlights
Object detection and classification has been very popular research area for many years
This paper proposed a novel system for noninvasive method of animal tracking and classification in a designated area
The system is based on intelligent devices with cameras, which are situated in a designated area, and a main computing unit (MCU) acting as a system master
Summary
Object detection and classification has been very popular research area for many years. The system is based on intelligent devices equipped with a camera, small computation unit like Raspberry Pi or Odroid-XU3, intelligent sensors and transmission module These devices are situated in the designated area, where it is necessary to detect animal movement and collect information. For people it is easy to see, track and classify moving objects in real life or in video sequence based on early experiences This object classification in computer vision is the task of recognizing a given object in the image or video sequence. To detect and track animals in video sequences, combination of background subtraction method and CAMShift algorithm was used In this part, a key frame extraction method was used in order to filtrate regions of interest and in this way improve accuracy in later object classification
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
