We present an autonomous system capable of performing security check routines. The surveillance machine, the
Clearpath Husky robotic platform, is equipped with three IP cameras with different orientations for the surveillance tasks
of face recognition, human activity recognition, autonomous navigation and 3D reconstruction of its environment.
Combining the computer vision algorithms onto a robotic machine has given birth to the Robust Artificial Intelligencebased
Defense Electro-Robot (RAIDER). The end purpose of the RAIDER is to conduct a patrolling routine on a single
floor of a building several times a day. As the RAIDER travels down the corridors off-line algorithms use two of the
RAIDER's side mounted cameras to perform a 3D reconstruction from monocular vision technique that updates a 3D
model to the most current state of the indoor environment. Using frames from the front mounted camera, positioned at
the human eye level, the system performs face recognition with real time training of unknown subjects. Human activity
recognition algorithm will also be implemented in which each detected person is assigned to a set of action classes
picked to classify ordinary and harmful student activities in a hallway setting.The system is designed to detect changes
and irregularities within an environment as well as familiarize with regular faces and actions to distinguish potentially
dangerous behavior. In this paper, we present the various algorithms and their modifications which when implemented
on the RAIDER serves the purpose of indoor surveillance.© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation
Binu M. Nair ; Yakov Diskin and Vijayan K. Asari
"Multi-modal low cost mobile indoor surveillance system on the Robust Artificial Intelligence-based Defense Electro Robot (RAIDER)", Proc. SPIE 8499, Applications of Digital Image Processing XXXV, 849918 (October 15, 2012); doi:10.1117/12.930353; http://dx.doi.org/10.1117/12.930353
