Abstract: This paper presents a novel multispectral stereo image correspondence approach. It is evaluated using a stereo rig constructed with a visible spectrum camera and a long wave infrared spectrum camera. The novelty of the proposed approach lies on the usage of Hough space as a correspondence search domain. In this way it avoids searching for correspondence in the original multispectral image domains, where information is low correlated, and a common domain is used. The proposed approach is intended to be used in outdoor urban scenarios, where images contain large amount of edges. These edges are used as distinctive characteristics for the matching in the Hough space. Experimental results are provided showing the validity of the proposed approach.

Abstract: Obstacle avoidance remains a challenging task for Micro Aerial Vehicles (MAV), due to their limited payload capacity to carry advanced sensors. Unlike larger vehicles, MAV can only carry light weight sensors, for instance a camera, which is our main assumption in this work. We explore passive monocular depth estimation and propose a novel method Position Aided Depth Estimation
(PADE). We analyse PADE performance and compare it against the extensively used Time To Collision (TTC). We evaluate the accuracy, robustness to noise and speed of three Optical Flow (OF) techniques, combined with both depth estimation methods. Our results show PADE is more accurate than TTC at depths between 0-12 meters and is less sensitive to noise. Our findings highlight the potential application of PADE for MAV to perform safe autonomous navigation in
unknown and unstructured environments.

Abstract: Among the components of a pedestrian detector, its trained pedestrian classifier is crucial for achieving the desired performance. The initial task of the training process consists in collecting samples of pedestrians and background, which involves tiresome manual annotation of pedestrian bounding boxes (BBs). Thus, recent works have assessed the use of automatically collected samples from photo-realistic virtual worlds. However, learning from virtual-world samples and testing in real-world images may suffer the dataset shift problem. Accordingly, in this paper we assess an strategy to collect samples from the real world and retrain with them, thus avoiding the dataset shift, but in such a way that no BBs of real-world pedestrians have to be provided. In particular, we train a pedestrian classifier based on virtual-world samples (no human annotation required). Then, using such a classifier we collect pedestrian samples from real-world images by detection. After, a human oracle rejects the false detections efficiently (weak annotation). Finally, a new classifier is trained with the accepted detections. We show that this classifier is competitive with respect to the counterpart trained with samples collected by manually annotating hundreds of pedestrian BBs.

Abstract: State-of-the-art deformable part-based models based on latent SVM have shown excellent results on human detection. In this paper, we propose to train a multiview deformable part-based model with automatically generated part examples from virtual-world data. The method is efficient as: (i) the part detectors are trained with precisely extracted virtual examples, thus no latent learning is needed, (ii) the multiview pedestrian detector enhances the performance of the pedestrian root model, (iii) a top-down approach is used for part detection which reduces the searching space. We evaluate our model on Daimler and Karlsruhe Pedestrian Benchmarks with publicly available Caltech pedestrian detection evaluation framework and the result outperforms the state-of-the-art latent SVM V4.0, on both average miss rate and speed (our detector is ten times faster).

Abstract: We present a novel technique for estimating initial 3D poses in the context of localization and Visual SLAM problems. The presented approach can deal with noise, outliers and a large amount of input data and still performs in real time in a standard CPU. Our method produces solutions with an accuracy comparable to those produced by RANSAC but can be much faster when the percentage of outliers is high or for large amounts of input data. On the current work we propose to formulate the pose estimation as an optimization problem on Lie groups, considering their manifold structure as well as their associated Lie algebras. This allows us to perform a fast and simple optimization at the same time that conserve all the constraints imposed by the Lie group SE(3). Additionally, we present several key design concepts related with the cost function and its Jacobian; aspects that are critical for the good performance of the algorithm.