Machine Learning Based Advanced Theft Detection Using YOLO and Tensor Flow with GUI
Vaibhav Deshbhratar 1, Prathmesh Lakhade 2, Shraddha Bisen 3 , Prachi Motghare4, Roshan Kolte5
1,2,3,4Students of Information Technology Department in KDK College of Engineering, Nagpur
5Assistance Professor of Information Technology Department in KDK College of Engineering, Nagpur
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Abstract - Object detection and tracking could be an immense, vivacious however inconclusive and trending area of computer vision. Due to its immense use in official surveillances, tracking modules applied in security and lots of other’s applications have made researchers to devise a lot of optimized and specialized methods. For validation purpose live input video will be taken for the same where objects will be getting detected and it can be simulated same for real-time through external hardware added. In the end we see the proper optimized and efficient algorithm for object detection and alert for security. Object Detection is computer vision technique used to detect object and identify its localization. This technique is not only used to identify the location but also to identify which type of object it is. This CV technique is used to detect objects in real time while maintaining the level of accuracy. By bringing some advancement in it, this system can be very helpful for people to keep track of their precious things or devices which are very expensive and need to be protected. Open CV (Open-Source Computer Vision Library) is a library of programming functions mainly aimed at real- time computer vision. Open CV features GPU acceleration for real-time operations. This feature helps us to write computationally intensive codes in C/C++ and create a Python wrapper for it so that we can use these wrappers as Python modules.
Keywords: Object detection, vivacious, YOLOv3, Tensor Flow, security, Tracking modules
