Automated Analysis of Visual Cues for Real World Monitoring Tasks

Conventional methods for inspection and monitoring using human operators are tedious, time consuming, and prone to errors. Technological innovations in the fields of image processing and machine vision have provided opportunities to automate these manual tasks in applications as diverse as surveillance, medical diagnostics, remote sensing, industrial quality control, and precision agriculture. Such automation can increase efficiency, productivity, effectiveness, speed, quality, and yield. Researchers over the years have investigated the feasibility, applications, and implications of different sensors and image processing algorithms, and now the use of low-cost sensors and mobile robots for monitoring and inspection applications is a reality. In this dissertation I focus on inspection and monitoring tasks that can be automated by applying image processing techniques to the output obtained from optical sensors (color and color-depth sensors). Although some of the industrial and military applications involving vision sensors for automation have already evolved into real-world products, there are many areas that still require thorough investigation and research for real-world implementation. I target applications that have societal importance in the areas of safety and precision agriculture. First, I present QuickBlaze, a flame and smoke detection system based on vision sensors aimed at early detection of fire incidents for open or closed indoor and outdoor environments. We use simple image and video processing techniques to compute motion and color cues, enabling segmentation of flame and smoke candidates from the background in real time. QuickBlaze does not require any offline training, although manual adjustment of parameters during a calibration phase is required to cater to the particular camera’s depth of view and the surrounding environment. In an extensive empirical evaluation benchmarking QuickBlaze against commercial fire detection software, we find that it has a better response time, is 2.66 times faster, and better localizes fire incidents. Detection of fire using our real-time video processing approach early on in the burning process holds the potential to decrease the length