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Title: | Design and Development of Surveillance System using OpenCV on Pandaboard |
Authors: | Doshi, Shalin M. |
Keywords: | EC 2012 Project Report Project Report 2012 EC Project Report EC (ES) Embedded Systems Embedded Systems 2012 12MEC 12MECE 12MECE07 |
Issue Date: | 1-Jun-2014 |
Publisher: | Institute of Technology |
Series/Report no.: | 12MECE07; |
Abstract: | Video surveillance has long been in use to monitor security sensitive areas such as banks, department stores, highways, crowded public places and borders. The advances in computing power, availability of large-capacity storage devices and high speed network infrastructure has paved the way for cheaper, multi sensor video surveillance systems. Traditionally, the video outputs are processed on-line by human operators and are usually saved to tapes for later use only after a forensic event. The increase in the number of cameras in ordinary surveillance systems overloaded both the human operators and the storage devices with high volumes of data and made it infeasible to ensure proper monitoring of sensitive areas for long times. In order to filter out redundant information generated by an array of cameras, and increase the response time to forensic events, assisting the human operators with identification of important events in video by the use of ``smart'' video surveillance systems has become a critical requirement. The making of video surveillance systems ``smart'' requires fast, reliable and robust algorithms for moving object detection, classification, tracking and activity analysis. In this thesis, a smart visual surveillance system with real-time moving object detection, classification and tracking capabilities is presented which uses OpenCV Library. OpenCV Library is an open source Intel research initiative to advance CPU intensive application which is mainly aimed at real time computer vision. The system operates on both color and gray scale video imagery from a stationary camera. It can handle object detection in indoor and outdoor environments and under changing illumination conditions. The classification algorithm makes use of the shape of the detected objects and temporal tracking results to successfully categorize objects into pre-defined classes like human, faced and other objects. After recognizing human it can also assist them on detection of predefined hand gesture. The work it does for assistance can be set to any Mechanical work, system commands on particular machine or any other work. In addition to these, Panda-board is used as a surveillance system with Ubuntu OS interface to camera sensor fixed to a predefined window and also supports TCP/IP socket network for information and file transfer. |
URI: | http://hdl.handle.net/123456789/4802 |
Appears in Collections: | Dissertation, EC (ES) |
Files in This Item:
File | Description | Size | Format | |
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12MECE07.pdf | 12MECE07 | 6.8 MB | Adobe PDF | ![]() View/Open |
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