This work presents a method for detecting abandoned objects in real-world conditions. The method presented here addresses the online and real time aspects of such systems, utilizes logic to differentiate between abandoned objects and stationary people, and is robust to temporary occlusion of potential abandoned objects. The capacity to not detect still people as abandoned objects is a major aspect that differentiates this work from others in the literature. Results are presented on 3 hours 36 minutes of footage over four videos representing both sparsely and densely populated real-world situations, also differentiating this work from others in the literature