Planar PØP: feature-less pose estimation with applications in UAV localization

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.We present a featureless pose estimation method that, in contrast to current Perspective-n-Point (PnP) approaches, it does not require n point correspondences to obtain the camera pose, allowing for pose estimation from natural shapes that do not necessarily have distinguished features like corners or intersecting edges. Instead of using n correspondences (e.g. extracted with a feature detector) we will use the raw polygonal representation of the observed shape and directly estimate the pose in the pose-space of the camera. This method compared with a general PnP method, does not require n point correspondences neither a priori knowledge of the object model (except the scale), which is registered with a picture taken from a known robot pose. Moreover, we achieve higher precision because all the information of the shape contour is used to minimize the area between the projected and the observed shape contours. To emphasize the non-use of n point correspondences between the projected template and observed contour shape, we call the method Planar PØP. The method is shown both in simulation and in a real application consisting on a UAV localization where comparisons with a precise ground-truth are provided.Peer ReviewedPostprint (author's final draft

Perception for detection and grasping

The final publication is available at link.springer.comThis research presents a methodology for the detection of the crawler used in the project AEROARMS. The approach consisted on using a two-step progressive strategy, going from rough detection and tracking, for approximation maneuvers, to an accurate positioning step based on fiducial markers. Two different methods are explained for the first step, one using efficient image segmentation approach; and the second one using Deep Learning techniques to detect the center of the crawler. The fiducial markers are used for precise localization of the crawler in a similar way as explained in earlier chapters. The methods can run in real-time.Peer ReviewedPostprint (author's final draft

A Survey on Joint Object Detection and Pose Estimation using Monocular Vision

In this survey we present a complete landscape of joint object detection and pose estimation methods that use monocular vision. Descriptions of traditional approaches that involve descriptors or models and various estimation methods have been provided. These descriptors or models include chordiograms, shape-aware deformable parts model, bag of boundaries, distance transform templates, natural 3D markers and facet features whereas the estimation methods include iterative clustering estimation, probabilistic networks and iterative genetic matching. Hybrid approaches that use handcrafted feature extraction followed by estimation by deep learning methods have been outlined. We have investigated and compared, wherever possible, pure deep learning based approaches (single stage and multi stage) for this problem. Comprehensive details of the various accuracy measures and metrics have been illustrated. For the purpose of giving a clear overview, the characteristics of relevant datasets are discussed. The trends that prevailed from the infancy of this problem until now have also been highlighted.Comment: Accepted at the International Joint Conference on Computer Vision and Pattern Recognition (CCVPR) 201

A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"

Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as "in-the-wild"). This is partially attributed to the fact that comprehensive "in-the-wild" benchmarks have been developed for face detection, landmark localisation and recognition/verification. A very important technology that has not been thoroughly evaluated yet is deformable face tracking "in-the-wild". Until now, the performance has mainly been assessed qualitatively by visually assessing the result of a deformable face tracking technology on short videos. In this paper, we perform the first, to the best of our knowledge, thorough evaluation of state-of-the-art deformable face tracking pipelines using the recently introduced 300VW benchmark. We evaluate many different architectures focusing mainly on the task of on-line deformable face tracking. In particular, we compare the following general strategies: (a) generic face detection plus generic facial landmark localisation, (b) generic model free tracking plus generic facial landmark localisation, as well as (c) hybrid approaches using state-of-the-art face detection, model free tracking and facial landmark localisation technologies. Our evaluation reveals future avenues for further research on the topic.Comment: E. Antonakos and P. Snape contributed equally and have joint second authorshi

A Novel Approach for Image Localization Using SVM Classifier and PSO Algorithm for Vehicle Tracking

In this paper, we propose a novel methodology for vehicular image localization, by incorporating the surveillance image object identification, using a local gradient model, and vehicle localization using the time of action. The aerial images of different traffic densities are obtained using the Histograms of Oriented Gradients (HOG) Descriptor. These features are acquired simply based on locations, angles, positions, and height of cameras set on the junction board. The localization of vehicular image is obtained based on the different times of action of the vehicles under consideration. Support Vector Machines (SVM) classifier, as well as Particle Swarm Optimization (PSO), is also proposed in this work. Different experimental analyses are also performed to calculate the efficiency of optimization methods in the new proposed system. Outcomes from experimentations reveal the effectiveness of the classification precision, recall, and F measure