![]() Finally, to obtain the key frame set, the SDM and IQM are again used to continuously filter the overlapping video sequences formed by the reference frame or the latest key frame. Then, the image quality module (IQM) obtains a reference frame from the above sequence by choosing high-quality images. They collaborate during the reference video sequence so that they can benefit from each other. ![]() The video stabilization module (VSM) selects a stable segment, while the shooting distance module (SDM) obtains a similar content scale. We model the three factors with three modules that are learned from an analysis of the shooting process. ![]() The method simultaneously analyzes different types of factors, namely, the video-level stability, image-level stability, and content scale stability, to take advantage of their complementary strengths. We present an effective video frame (including reference frame and key frames) acquisition method for image stitching. Quantitative and qualitative comparisons with state-of-the-art algorithms show the robustness and several orders of faster of our technique. To evaluate the proposed approach, we experiment on the large-scale VIRATaerial surveillance dataset, which is challenging for its heterogeneity in image quality and diversity of the scene. We develop an effective graph-based framework that can robustly conduct the grouping, retrieval and stitching tasks. We exploit this property and treat the aerial image stitching problem as temporal sequential grouping and spatial cross-group retrieval. This monitoring mechanism yields to multiple short, successive video clips that overlap in either time or space. ![]() Often, human operators apply continuous monitoring of the drone to revisit the same area of interest. This paper presents a hybrid stitching method based on the observation that aerial videos captured in real surveillance settings are neither totally ordered nor completely unordered. Diverse scene stitching is a challenging task in aerial video surveillance. ![]()
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