Development and Validation of a Camera-Based Illumination Controller for People Detection, Tracking and Recognition using Computational Intelligence

Abstract

Illumination is crucial in human activities and in machine vision applications. For indoor surveillance applications, Infrared (IR) Light Emitting Diodes (LEDs) are the common means of providing illumination to the camera to cause no discomfort to human occupants. While IR provides non-obtrusive illumination for the camera, the same energy consumed does not provide the illumination to indoor spaces of the building. This is important if the premises where the camera is installed is not connected to the main power source or electric grid but derives energy from renewable sources. In this work, an illumination controller based on fuzzy logic system is developed and integrated to a vision system and an LED lighting system to provide a constant level of illumination to an object regardless of its distance from the image sensor. The computer vision system performs human object detection and face recognition and outputs fuzzy values representing the inferred distance of detected objects where the fuzzy system generates crisp output of duty cycle settings for the PWM controller for the LED lighting system to provide the required illumination needed by the vision system. Optimum illumination level for the vision system to perform the detection, tracking and face recognition operations must be provided by the system. Using visible Light Emitting Diodes as source of illumination, the system provides illumination both for the proper operation of the camera and human personnel monitoring the premises where the system is installed. This feature is significant in energy-constrained surveillance applications or where there is no power source derived from the electric grid.