ECE 172A SIMPLE OBJECT DETECTOR WITH INDICATOR WHEN A NEW OBJECT HAS BEEN ADDED TO OR MISSING IN A ROOM Presented by by Hugo Groening

INTRODUCTION In the security industry, there has always been the demand for simple, accurate and economical systems that allow the prevention or ability to track through footage when a security breach has occurred. The project to be presented targets locations such as museums, stores, homes, Banks, etc.

AGENDA Objective Objective Related Research Related Research Method Method Results Results Future work or improvements Future work or improvements Conclusion Conclusion

Objective - To create an object recognition and detector in a room using AVI files. - To train detector to indicate when an object is missing or possibly about to be taken.

Related Work and Research References: - A. Torralba, K. P. Murphy and W. T. Freeman. (2004). "Sharing features: efficient boosting procedures for multiclass object detection". Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). pp B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman. Labelme: a database and web- based tool for image annotation. Technical Report AIM , MIT AI Lab Memo, September, Can be found at: Location of Code and Database: Industry - Geovision: - Geovision:

Goal Edited from Geovision Inc.

Method Detect if object count changes from previous frame Nothing change, keeps counting Choose Video to be analyzed Choose Video to be analyzed Look at 1 st frame to find size Initialize Video output Initialize Video output loops into frames and makes copy of current frame loops into frames and makes copy of current frame Count Objects in frame Find centroid of objects and tracks them Find centroid of objects and tracks them Change to Grey and invert to BW Change to Grey and invert to BW Erodes, dilates If different WARNING

Results -Count Objects -Find Objects -Warns when object count changes -Storages Video Output for Future analysis

Limitations - Hard drive Space for memory allocation of Database - Processor - AVI files taken from other video cameras - Objects and background color - Time frame

Output Footage

Future Work or Improvements - Minimize the memory size of Database - Funding for continuation of Labelme Database - Access to individual Objects within Database - Research other Algorithm options for Object detection and Classification - Find more adequate video Recorder for surveillance purposes - Live Object tracking detector - To become more knowledgeable with Matlab and other Programming languages.

Conclusion The background and knowledge obtained through the practice of the information previously researched, had developed a strong awareness of how digital image processing can be directly applied to needs in society. In even the most simple cases, such as in the security or surveillance industry, high end technology will be needed for accuracy. Continuous development and improvements will also be necessary.