Embedded smart camera systems are gaining popularity for a number of real world surveillance applications. However, there are still challenges, i.e. variation in illumination, shadows, occlusion, and weather conditions while employing the vision algorithms in outdoor environments. For safety-critical surveillance applications, the visual sensors can be complemented with beyond-visual-range sensors. This in turn requires analysis, development and modification of existing imaging techniques. In this work, a low complexity background modelling and subtraction technique has been proposed for thermal imagery. The proposed technique has been implemented on Field Programmable Gate Arrays (FPGAs) after in-depth analysis of different sets of images, characterizing poor signal-to-noise ratio challenges, e.g. motion of high frequency background objects, temperature variation and camera jitter etc. The proposed technique dynamically updates the background on pixel level and requires a single frame storage as opposed to existing techniques. The comparison of this approach with two other approaches show that this approach performs better in different environmental conditions. The proposed technique has been modelled in Register Transfer Logic (RTL) and implementation on the latest FPGAs shows that the design requires less than 1 percent logics, 47 percent block RAMs, and consumes 91 mW power consumption on Artix-7 100T FPGA.

Safety-critical applications require robust and real-time surveillance. For such applications, a vision sensor alone can give false positive results because of poor lighting conditions, occlusion, or different weather conditions. In this work, a visual sensor is complemented by an infrared thermal sensor which makes the system more resilient in unfavorable situations. In the proposed camera architecture, initial data intensive tasks are performed locally on the sensor node and then compressed data is transmitted to a client device where remaining vision tasks are performed. The proposed camera architecture is demonstrated as a proof-ofconcept and it offers a generic architecture with better surveillance while only performing low complexity computations on the resource constrained devices.