The moving objects detection from freely moving camera like the one mounted on Unmanned Aerial Vehicle (UAV) stands as an important and challenging issue. This paper introduced a new MOD-IR method for moving objects detection from UAV-captured video sequences. The proposed method consists of four steps: (1) feature extraction and matching, (2) frame registration, (3) moving objects detection and (4) moving objects detection post-processing. Our method stands out from those of the literature in a number of ways. First, we enhanced the method effectiveness and robustness by handling the constraints related to this field through extracting robust features, on the one hand, and automatically defining the optimum threshold, on the other. Second, we proposed an efficient method able to deal with real-time applications by extracting keypoint features instead of pixel-to-pixel model estimation, and by simulating the search for the matching features among multiple trees. Finally, we involved the quick-shift segmentation in parallel with the three first steps, in order to enhance and accelerate the moving objects detection task. Relying on quantitative and qualitative evaluations of the proposed method on a variety of sequences extracted from several datasets (such as DARPA VIVID-EgTest05, Hopkins 155, UCF Aerial Action, etc.), we assessed the performance of our method compared to the state-of-the-art reference methods. Furthermore, the time cost evaluation has enabled us to emphasize that our MOD-IR method is the optimal choice for real-time applications, owing to its lower computational time requirement compared to the reference methods.
