Moving target detection plays a primary and pivotal role in avionics visual analysis, which aims to completely and accurately detect moving objects from complex backgrounds. However, due to the relatively small sizes of targets in aerial video, many deep networks that achieve success in normal size object detection are usually accompanied by a high rate of false alarms and missed detections. To address this problem, we propose a novel visual detail augmented mapping approach for small aerial target detection. Concretely, we first present a multi-cue foreground segmentation algorithm including motion and grayscale information to extract potential regions. Then, based on the visual detail augmented mapping approach, the regions that might contain moving targets are magnified to multi-resolution to obtain detailed target information and rearranged into new foreground space for visual enhancement. Thus, original small targets are mapped to a more efficient foreground augmented map which is favorable for accurate detection. Finally, driven by the success of deep detection network, small moving targets can be well detected from aerial video. Experiments extensively demonstrate that the proposed method achieves success in small aerial target detection without changing the structure of the deep network. In addition, compared with the-state-of-art object detection algorithms, it performs favorably with high efficiency and robustness.

In this paper, we investigate the problem of aligning multiple deployed camera into one united coordinate system for cross-camera information sharing and intercommunication. However, the difficulty is greatly increased when faced with large-scale scene under chaotic camera deployment. To address this problem, we propose a UAV-assisted wide area multi-camera space alignment approach based on spatiotemporal feature map. It employs the great global perception of Unmanned Aerial Vehicles (UAVs) to meet the challenge from wide-range environment. Concretely, we first present a novel spatiotemporal feature map construction approach to represent the input aerial and ground monitoring data. In this way, the motion consistency across view is well mined to overcome the great perspective gap between the UAV and ground cameras. To obtain the corresponding relationship between their pixels, we propose a cross-view spatiotemporal matching strategy. Through solving relative relationship with the above air-to-ground point correspondences, all ground cameras can be aligned into one surveillance space. The proposed approach was evaluated in both simulation and real environments qualitatively and quantitatively. Extensive experimental results demonstrate that our system can successfully align all ground cameras with very small pixel error. Additionally, the comparisons with other works on different test situations also verify its superior performance.

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Synthetic aperture imaging, which has been proved to be an effective approach for occluded object imaging, is one of the challenging problems in the field of computational imaging. Currently most of the related researches focus on fixed synthetic aperture which usually accompanies with mixed observation angle and foreground de-focus blur. But the existence of them is frequently a source of perspective effect decrease and occluded object imaging quality degradation. In order to solve this problem, we propose a novel data-driven variable synthetic aperture imaging based on semantic feedback. The semantic content we concerned for better de-occluded imaging is the foreground occlusions rather than the whole scene. Therefore, unlike other methods worked on pixel-level, we start from semantic layer and present a semantic labeling method based on feedback. Semantic labeling map deeply mines visual data in synthetic image and preserves the semantic information of foreground occluder. On the basis of semantic feedback strategy, semantic labeling map will conversely pass to synthetic imaging process. The proposed data-driven variable synthetic aperture imaging contains two levels: one is adaptive changeable imaging aperture driven by synthetic depth and perspective angle, the other is light ray screening driven by visual information in semantic labeling map. On this basis, the hybrid camera view and superimposition of foreground occlusion can be removed. Evaluations on several complex indoor scenes and real outdoor environments demonstrate the superiority and robustness performance of our proposed approach. INDEX TERMS Synthetic aperture imaging, data-driven variable synthetic aperture, semantic feedback imaging, multi-camera array.

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