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Abstract. In this work we evaluated how the use of different positioning systems affects the accuracy of Digital Elevation Models (DEMs) generated from aerial imagery obtained with Unmanned Aerial Vehicles (UAVs). In this domain, state-of-the-art DEM generation algorithms suffer from typical errors obtained by GPS/INS devices in the position measurements associated with each picture obtained. The deviations from these measurements to real world positions are about meters. The experiments have been carried out using a small quadrotor in the indoor testbed at the Center for Advanced Aerospace Technologies (CATEC). This testbed houses a system that is able to track small markers mounted on the UAV and along the scenario with millimeter precision. This provides very precise position measurements, to which we can add random noise to simulate errors in different GPS receivers. The results showed that final DEM accuracy clearly depends on the positioning information.

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This work proposes the volume integral (VI) as a new descriptor for three-dimensional action recognition. The descriptor transforms the actor's volumetric information into a two-dimensional representation by projecting the voxel data to a set of planes that maximize the discrimination of actions. Our descriptor significantly reduces the amount of data of the three-dimensional representations yet preserves the most important information. As a consequence, the action recognition process is greatly speeded up while achieving very high success rates. The method proposed is therefore especially appropriate for applications in which limitations of computing power and space are significant aspects to consider, such as real-time applications or mobile devices. Additionally, the descriptor is sensitive to reflected actions, i.e., same actions performed with different limbs can be differentiated. This paper tests the VI using several Dimensionality Reduction techniques (namely PCA, 2D-PCA, LDA) and different Machine Learning approaches (namely Clustering, SVM and HMM) so as to determine the best combination of these for the action recognition task. Experiments conducted on the public IXMAS dataset show that the VI compares favorably with state-of-the-art descriptors both in terms of classification rates and computing times.