Students: Anthony Kang, Luke Neumann, Erik Rozolis Professors: Dr. Edward J. Delp, 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 Environmental trends (rising sea levels, shorter days, etc.) System Team Progress Database improved for easier data management System redesigned to handle a more diverse set of applications Locations of cameras found so far mapped and clustered (below) Implemented Django for better website management and easier modification Camera search given more specific options for faster loading Query improved for faster map loading 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 API The image processing API is designed for image processing algorithm testers to use our system easily. All they need to do is just write a pure processing algorithm and plug it into the API. Tasks for the API 1.Download images for cameras and store them into a buffer. 2.Send Reports to the system. 3.Run the image processing algorithm. Future Development Short Term: Improve camera search function Scale up from 7 worker computers and 30,000 cameras to hundreds of workers and hundred thousands of cameras Build API which will interface with a website Improve rain detection and sunrise/sunset algorithm Long Term: Scale up to millions of cameras and thousands of workers Upgrade to billable system Develop 4 or 5 weather detection algorithms Rain Detection algorithm This algorithm analyzes videos from cameras and detect if it is raining. Fig.6 Weight of Rain Fig.2 Cloudy Fig.3 Rain Fig4 weight of sunny Fig.5 Weight of cloudy Fig.1 Sunny Sunrise/Sunset Detection This algorithm analyzes videos from cameras and detect if it is sun rising or sun setting. From the result retrieved, the length of the day can be calculated. Steps 1. Detect Horizon 2. Calculate brightness of sky using pixels above horizon 3. Store value and repeat 4. Sudden increase in brightness in morning may indicate sunrise. 5. Sudden decrease in brightness in evening may indicate sunset. Time: 20:42 RGB Avg: 52 Time: 20:39 RGB Avg: 71Time: 20:31 RGB Avg: 111 sunset Rain Detection results Automatic Testing and Integration Use Jenkins and Github to test and integrate new code Protects the main site from breaking due to new code