Nowadays, the amount of video data acquired for observation or surveillance applications is overwhelming. Due to these huge volumes of video data, focusing the attention of operators on "areas of interest" requires change detection algorithms. In the particular task of aerial observation, camera motion and viewpoint differences introduce parallax effects, which may substantially affect the reliability and the efficiency of automatic change detection.In this paper, we introduce a novel approach for change detection that considers the geometric aspects of camera sensors as well as the statistical properties of changes. Indeed, our method is based on optical flow matching, constrained by the epipolar geometry, and combined with a statistical change decision criterion. The good performance of our method is demonstrated through our new public Aerial Imagery Change Detection (AICD) dataset of labeled aerial images.

Aerial image change detection is highly dependent on the accuracy of camera pose and may be subject to false alarms caused by misregistrations. In this paper, we present a novel pose estimation approach based on Visual Servoing that combines aerial videos with 3D models.Firstly, we introduce a formulation that relates image registration with the poses of a moving camera observing a 3D plane. Then, we combine this formulation with Newton's algorithm in order to estimate camera poses in a given aerial video. Finally, we present and discuss experimental results which demonstrate the robustness and the accuracy of our method.

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With the growing capacity of video devices, human operators are nowadays overwhelmed by the huge volumes of data generated in different applications including surveillance. Therefore, automatic video processing techniques are required in order to filter out uninteresting data and to focus the attention of operators. However, reliability is still a challenging problem.In this paper, we show how spatio-temporal redundancy may be exploited to enhance the accuracy of automatic change detection in aerial videos. More precisely, we present an algorithm based on Belief Propagation in order to improve spatio-temporal consistency between successive change detection results. Experiments demonstrate that our method leads to increased accuracy in change detection.

This paper presents methods for performing realtime semantic SLAM aimed at autonomous navigation and control of a humanoid robot in a manufacturing scenario. A novel multi-keyframe approach is proposed that simultaneously minimizes a semantic cost based on class-level features in addition to common photometric and geometric costs. The approach is shown to robustly construct a 3D map with associated class labels relevant to robotic tasks. Alternatively to existing approaches, the segmentation of these semantic classes have been learnt using RGB-D sensor data aligned with an industrial CAD manufacturing model to obtain noisy pixel-wise labels. This dataset confronts the proposed approach in a complicated real-world setting and provides insight into the practical use case scenarios. The semantic segmentation network was fine tuned for the given use case and was trained in a semisupervised manner using noisy labels. The developed software is real-time and integrated with ROS to obtain a complete semantic reconstruction for the control and navigation of the HRP4 robot. Experiments in-situ at the Airbus manufacturing site in Saint-Nazaire validate the proposed approach.