Detection, location and grasping objects using a stereo sensor on UAV in outdoor environments

The article presents a vision system for the autonomous grasping of objects with Unmanned Aerial Vehicles (UAVs) in real time. Giving UAVs the capability to manipulate objects vastly extends their applications, as they are capable of accessing places that are difficult to reach or even unreachable for human beings. This work is focused on the grasping of known objects based on feature models. The system runs in an on-board computer on a UAV equipped with a stereo camera and a robotic arm. The algorithm learns a feature-based model in an offline stage, then it is used online for detection of the targeted object and estimation of its position. This feature-based model was proved to be robust to both occlusions and the presence of outliers. The use of stereo cameras improves the learning stage, providing 3D information and helping to filter features in the online stage. An experimental system was derived using a rotary-wing UAV and a small manipulator for final proof of concept. The robotic arm is designed with three degrees of freedom and is lightweight due to payload limitations of the UAV. The system has been validated with different objects, both indoors and outdoor

SwimmerNET: Underwater 2D Swimmer Pose Estimation Exploiting Fully Convolutional Neural Networks

Professional swimming coaches make use of videos to evaluate their athletes' performances. Specifically, the videos are manually analyzed in order to observe the movements of all parts of the swimmer's body during the exercise and to give indications for improving swimming technique. This operation is time-consuming, laborious and error prone. In recent years, alternative technologies have been introduced in the literature, but they still have severe limitations that make their correct and effective use impossible. In fact, the currently available techniques based on image analysis only apply to certain swimming styles; moreover, they are strongly influenced by disturbing elements (i.e., the presence of bubbles, splashes and reflections), resulting in poor measurement accuracy. The use of wearable sensors (accelerometers or photoplethysmographic sensors) or optical markers, although they can guarantee high reliability and accuracy, disturb the performance of the athletes, who tend to dislike these solutions. In this work we introduce swimmerNET, a new marker-less 2D swimmer pose estimation approach based on the combined use of computer vision algorithms and fully convolutional neural networks. By using a single 8 Mpixel wide-angle camera, the proposed system is able to estimate the pose of a swimmer during exercise while guaranteeing adequate measurement accuracy. The method has been successfully tested on several athletes (i.e., different physical characteristics and different swimming technique), obtaining an average error and a standard deviation (worst case scenario for the dataset analyzed) of approximately 1 mm and 10 mm, respectively

Trajectory-based Human Action Recognition

Human activity recognition has been a hot topic for some time. It has several challenges, which makes this task hard and exciting for research. The sparse representation became more popular during the past decade or so. Sparse representation methods represent a video by a set of independent features. The features used in the literature are usually lowlevel features. Trajectories, as middle-level features, capture the motion of the scene, which is discriminant in most cases. Trajectories have also been proven useful for aligning small neighborhoods, before calculating the traditional descriptors. In fact, the trajectory aligned descriptors show better discriminant power than the trajectory shape descriptors proposed in the literature. However, trajectories have not been investigated thoroughly, and their full potential has not been put to the test before this work. This thesis examines trajectories, defined better trajectory shape descriptors and finally it augmented trajectories with disparity information. This thesis formally define three different trajectory extraction methods, namely interest point trajectories (IP), Lucas-Kanade based trajectories (LK), and Farnback optical flow based trajectories (FB). Their discriminant power for human activity recognition task is evaluated. Our tests reveal that LK and FB can produce similar reliable results, although the FB perform a little better in particular scenarios. These experiments demonstrate which method is suitable for the future tests. The thesis also proposes a better trajectory shape descriptor, which is a superset of existing descriptors in the literature. The examination reveals the superior discriminant power of this newly introduced descriptor. Finally, the thesis proposes a method to augment the trajectories with disparity information. Disparity information is relatively easy to extract from a stereo image, and they can capture the 3D structure of the scene. This is the first time that the disparity information fused with trajectories for human activity recognition. To test these ideas, a dataset of 27 activities performed by eleven actors is recorded and hand labelled. The tests demonstrate the discriminant power of trajectories. Namely, the proposed disparity-augmented trajectories improve the discriminant power of traditional dense trajectories by about 3.11%

Pedestrian detection and tracking using stereo vision techniques

Automated pedestrian detection, counting and tracking has received significant attention from the computer vision community of late. Many of the person detection techniques described so far in the literature work well in controlled environments, such as laboratory settings with a small number of people. This allows various assumptions to be made that simplify this complex problem. The performance of these techniques, however, tends to deteriorate when presented with unconstrained environments where pedestrian appearances, numbers, orientations, movements, occlusions and lighting conditions violate these convenient assumptions. Recently, 3D stereo information has been proposed as a technique to overcome some of these issues and to guide pedestrian detection. This thesis presents such an approach, whereby after obtaining robust 3D information via a novel disparity estimation technique, pedestrian detection is performed via a 3D point clustering process within a region-growing framework. This clustering process avoids using hard thresholds by using bio-metrically inspired constraints and a number of plan view statistics. This pedestrian detection technique requires no external training and is able to robustly handle challenging real-world unconstrained environments from various camera positions and orientations. In addition, this thesis presents a continuous detect-and-track approach, with additional kinematic constraints and explicit occlusion analysis, to obtain robust temporal tracking of pedestrians over time. These approaches are experimentally validated using challenging datasets consisting of both synthetic data and real-world sequences gathered from a number of environments. In each case, the techniques are evaluated using both 2D and 3D groundtruth methodologies

Assistive Technology and Biomechatronics Engineering

This Special Issue will focus on assistive technology (AT) to address biomechanical and control of movement issues in individuals with impaired health, whether as a result of disability, disease, or injury. All over the world, technologies are developed that make human life richer and more comfortable. However, there are people who are not able to benefit from these technologies. Research can include development of new assistive technology to promote more effective movement, the use of existing technology to assess and treat movement disorders, the use and effectiveness of virtual rehabilitation, or theoretical issues, such as modeling, which underlie the biomechanics or motor control of movement disorders. This Special Issue will also cover Internet of Things (IoT) sensing technology and nursing care robot applications that can be applied to new assistive technologies. IoT includes data, more specifically gathering them efficiently and using them to enable intelligence, control, and new applications

Human Pose Estimation from Monocular Images : a Comprehensive Survey

Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problema into several modules: feature extraction and description, human body models, and modelin methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used