Video Surveillance Capturing, Management and Analysis of Security Videos. -AbhinavGoel -VarunVarshney
Introduction-why Surveillance at IIIT ? For the implementation of all the above and more analysis schemes the biggest need of the hour is : A scalable ,efficient and a robust system. That is where we jump in !
Modular • Scalable Video Surveillance System : Design Objectives System Necessity: Modular: System should be divided into smaller parts so that chances of failure reduce. Layered Architecture. Robust: Should be resistant to crashes.Reliable. For eg. In case of Failure : Error Reporting to admin Scalable : More external storage devices can be easily added. ? admin ?
Continuous Camera Capture. Web Server • Web Server • Hosts the interactive front end which has a user friendly interface to access the ‘Video Processing Results’. • Sends a request a to the CCS to send the captured videos (which are temporarily stored here) to the ‘Processing Station’. • Sends a request to CCS to search for a user requested video. • Video Processing Station • Responsible for receiving videos from Web Server and running various Video Processing Algorithms. • Sends a request to CCS to store the result in a suitable available storage device. • CCS • Master Controller of the whole System. • Accepts /Sends requests to other stations. • Stores the meta-data corresponding to the current state of the system .(storage devices available, processing state etc.) • Stores complete meta-data of the processing results and their location. System Design : Modules Continuous Camera Capture Any camera can be installed which can be integrated with OpenCV. The video capturing goes on continuously for 24 Hrs. Central Control Server(CCS) Storage Devices Video Processing Station
Segmentation Process Further large scale Video Processing System Process Continuous Incoming Frames Segmentation Process No of white pixels=n. If there are sufficient number of boxes with total white pixels greater than a threshold => activity frame.Capturing continues till a threshold amount of 'non-activity' frames are found.
System Process Comparison with other techniques: It is better than Using a static image and doing background subtraction as the activity is studied at a box level. User End: 1)A robust ,scalable system. 2) User can track all the activity videos by using a timeline. 3) A detailed census of the traffic is also available to the user along with a log of the recent activity. 4) Further features like a gallery of the faces captured in a shot are also visible(usage of OpenCV face detector)
System Demo See Demo Video : (link on the website) or