Region of Interest (ROI) coding is a common method for data reduction in scenarios where bandwidth is crucial like in aerial video surveillance from Unmanned Aerial Vehicles (UAVs). In order to save bits, non-ROI areas are typically reduced in quality or not transmitted at all and thus, an accurate ROI classification is mandatory. Moving objects (MOs) are often considered as ROIs and consequently have to be accurately detected onboard. However, common detection approaches either rely on computationally demanding processing which is not available at small UAVs with only limited energy, are model based or cannot provide a sufficient detection precision. While not detected MOs lead to a degraded representation at the decoder, erroneously detected MOs lead to an unnecessary high bit rate. We tackle all these issues utilizing an efficient object proposal computation. Based on a dual-threshold strategy applied to image differences, we propose a linear prediction-supported block matcher. Compared to a simple thresholding approach, it shows superior performance and is robust to threshold tuning. By integrating superpixels into the framework, we further recover the complete shape of the MOs. Finally, an efficient tracking-by-detection system is employed to produce accurate detections from the proposals, thereby recovering missed MOs and denying wrong proposals, making the coding more efficient. We achieve an improved detection precision of up to 76 % compared to a simple difference image-based approach. By using a general ROI coding framework we reduce the bit rate of our test set by 70 % compared to common HEVC.