Gait based people identification system using multiple switching kinects

Unobtrusive people identification based on gait recognition is steadily growing attention in modern video surveillance systems. In this paper we propose a two tier architecture for gait based people identification. The proposed architecture employs multiple Kinects working together as an integrated system to broaden the coverage area of surveillance. An intelligent triggering mechanism is presented for switching on and off the infrared (IR) cameras of different Kinects for optimum resource utilization. For efficient utilization of network bandwidth, the skeleton data from all the Kinects' edge devices (controllers) are compressed and sent to a backend server for people identification. Experimental results show that the triggering mechanism is capable of reducing the average power consumption of the Kinect controllers by more than 40 percent. Results also indicate that the person recognition accuracy does not degrade with the usage of the data compression, which reduces the data rate by 4 to 6 times while preserving their statistical properties.