Study on models for smart surveillance through multi-camera networks

Abstract

With ever changing world, visual surveillance once a distinctive issue has now became an indispensable component of surveillance system and multi-camera network are the most suited way to achieve them. Even though multi-camera network has manifold advantage over single camera based surveillance, still it adds overheads towards processing, memory requirement, energy consumption, installation costs
and complex handling of the system.This thesis explores different challenges in the domain of multi-camera network
and surveys the issue of camera calibration and localization. The survey presents an in-depth study of evolution of camera localization over the time. This study helps in realizing the complexity as well as necessity of camera localization in multi-camera network.This thesis proposes smart visual surveillance model that study phases of multi-camera network development model and proposes algorithms at the level of camera placement and camera control. It proposes camera placement technique for gait pattern recognition and a smart camera control governed by occlusion determination algorithm that leads to reducing the number of active camera thus eradicating many overheads yet not compromising with the standards of surveillance.
The proposed camera placement technique has been tested over self-acquired data from corridor of Vikram Sarabhai Hall of Residence, NIT Rourkela. The proposed algorithm provides probable places for camera placement in terms of 3D
plot depicting the suitability of camera placement for gait pattern recognition.The control flow between cameras is governed by a three step algorithm that works on direction and apparent speed estimation of moving subjects to determine the chances of occlusion between them. The algorithms are tested over self-acquired as well as existing gait database CASIA Dataset A for direction determination as well as occlusion estimation.