Autonomously detecting novelties using background subtraction has quickly become a very important area of image analysis with many different approaches to novelty detection and the output therein. The ultimate goal of the approaches is to be robust to false detections and noise whilst using as little computational power as possible. This review focuses on some of the most prominent pixel-wise background subtraction techniques currently in use, and compares and contrasts their attributes and capabilities. The purpose of this review is to practically summarize the pixel-wise approaches and suggest a way forward from these techniques.