1. Students: Aiman Md Uslim, Jin Bai, Sam Yellin, Laolu Peters Professors: Dr. Yung-Hsiang Lu CAM 2 Continuous Analysis of Many CAMeras The Problem Currently there is no common computing infrastructure which can support continuous analysis of many internet connected cameras and so researchers are unable to harness this vast amount of data for analysis. The Infrastructure 1.A large number of publicly available cameras and their properties 2.A set of functions common to different analysis programs and an API for developing the analysis programs 3.A resource manager (system) which optimizes the resources needed for running the analysis programs Possible Applications Uses of the system include: Traffic analysis to improve congestion or detect accidents Weather observation to increase the accuracy of existing weather models Population detection and areas of high User Interface Team Purpose: Further develop the CAM 2 web application Progress: Live stream data from server to application Display of all IP streams for test cameras Menu displaying grid of camera tiles Cloud-Based Distributed System Architecture Above is a lower level view of the interactions of the distributed system from application upload to the job scheduler. Two monitors provide information about the performance of the system and the data flow during operation. A database holds everything from logins for the website to reports generated automatically by the manager. Image Processing Team Purpose: Publishing a research paper for the IEEE International Conference on Image Processing Paper’s Aim: To compare the efficiency of different classification algorithms when tested using images with different properties. Progress: Implementation of indoor vs outdoor classification algorithm using straightness of edges Creating user interface for classifying images according to its special properties In progress of finding new papers to be implemented for analyzing the data Future Development Short Term: Continue improvements on web application Bring total camera count up to one-hundred thousand Continue optimizations on heat map generation and detection Long Term: Scale up to millions of cameras and thousands of workers Population graphing in real time Automated testing Large scale implementation of detection and analysis Data Integrity and Testing Purpose: develop testing infrastructure and explore video transmission technologies Progress: Implementation of static server using NGINX for data storage Serve archived videos and images from server Optimized RAID configurations Transmission speed experiments and optimizations Camera Team Purpose: Understand and integrate existing camera infrastructure within CAM 2 Progress: Thousands of cameras added to database Capture of JavaScript-generated URLs Updates to database of cameras Straightness of Edges The algorithm looks at edges that are found in the image and calculates the percentage of straightness of the edges using a certain formulation. The hypothesis is that indoor images has more synthetic objects that possess straight edges property, while outdoor images has less of them. Indoor vs Outdoor Edge Detected Comparison