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Video sensors are ubiquitous in the realm of security and defense. Successive image data from those sensors can serve as an integral part of early-warning systems by drawing attention to suspicious anomalies. Using object detection, computer vision, and machine learning to automate some of those detection and classification tasks aids in maintaining a consistent level of situational awareness in environments with ever-present threats. Specifically, the ability to detect small objects in video feeds would help people and systems to protect themselves against far away or small hazards. This work proposes a way to accentuate features in video stills by subtracting pixels from surrounding frames to extract motion information. Features extracted from a sequence of frames can be used either alone, or that signal can be concatenated onto the original image to highlight a moving object of interest. Using a two-stage object detector, we explore the impacts of frame differencing on Drone vs. Bird videos from both stationary cameras as well as cameras that pan and zoom. Our experiments demonstrate that this algorithm is capable of detecting objects that move in a scene regardless of the state of the camera.