Metaheuristic Optimization Techniques for Articulated Human Tracking

Four adaptive metaheuristic optimization algorithms are proposed and demonstrated: Adaptive Parameter Particle Swarm Optimization (AP-PSO), Modified Artificial Bat (MAB), Differential Mutated Artificial Immune System (DM-AIS) and hybrid Particle Swarm Accelerated Artificial Immune System (PSO-AIS). The algorithms adapt their search parameters on the basis of the fitness of obtained solutions such that a good fitness value favors local search, while a poor fitness value favors global search. This efficient feedback of the solution quality, imparts excellent global and local search characteristic to the proposed algorithms. The algorithms are tested on the challenging Articulated Human Tracking (AHT) problem whose objective is to infer human pose, expressed in terms of joint angles, from a continuous video stream. The Particle Filter (PF) algorithms, widely applied in generative model based AHT, suffer from the 'curse of dimensionality' and 'degeneracy' challenges. The four proposed algorithms show stable performance throughout the course of numerical experiments. DM-AIS performs best among the proposed algorithms followed in order by PSO-AIS, AP-PSO, and MBA in terms of Most Appropriate Pose (MAP) tracking error. The MAP tracking error of the proposed algorithms is compared with four heuristic approaches: generic PF, Annealed Particle Filter (APF), Partitioned Sampled Annealed Particle Filter (PSAPF) and Hierarchical Particle Swarm Optimization (HPSO). They are found to outperform generic PF with a confidence level of 95%, PSAPF and HPSO with a confidence level of 85%. While DM-AIS and PSO-AIS outperform APF with a confidence level of 80%. Further, it is noted that the proposed algorithms outperform PSAPF and HPSO using a significantly lower number of function evaluations, 2500 versus 7200. The proposed algorithms demonstrate reduced particle requirements, hence improving computational efficiency and helping to alleviate the 'curse of dimensionality'. The adaptive nature of the algorithms is found to guide the whole swarm towards the optimal solution by sharing information and exploring a wider solution space which resolves the 'degeneracy' challenge. Furthermore, the decentralized structure of the algorithms renders them insensitive to accumulation of error and allows them to recover from catastrophic failures due to loss of image data, sudden change in motion pattern or discrete instances of algorithmic failure. The performance enhancements demonstrated by the proposed algorithms, attributed to their balanced local and global search capabilities, makes real-time AHT applications feasible. Finally, the utility of the proposed algorithms in low-dimensional system identification problems as well as high-dimensional AHT problems demonstrates their applicability in various problem domains

Unmanned Robotic Systems and Applications

This book presents recent studies of unmanned robotic systems and their applications. With its five chapters, the book brings together important contributions from renowned international researchers. Unmanned autonomous robots are ideal candidates for applications such as rescue missions, especially in areas that are difficult to access. Swarm robotics (multiple robots working together) is another exciting application of the unmanned robotics systems, for example, coordinated search by an interconnected group of moving robots for the purpose of finding a source of hazardous emissions. These robots can behave like individuals working in a group without a centralized control

Model-based human upper body tracking using interest points in real-time video

Vision-based human motion analysis has received huge attention from researchers because of the number of applications, such as automated surveillance, video indexing, human machine interaction, traffic monitoring, and vehicle navigation. However, it contains several open problems. To date, despite very promising proposed approaches, no explicit solution has been found to solve these open problems efficiently. In this regard, this thesis presents a model-based human upper body pose estimation and tracking system using interest points (IPs) in real-time video. In the first stage, we propose a novel IP-based background-subtraction algorithm to segment the foreground IPs of each frame from the background ones. Afterwards, the foreground IPs of any two consecutive frames are matched to each other using a dynamic hybrid localspatial IP matching algorithm, proposed in this research. The IP matching algorithm starts by using the local feature descriptors of the IPs to find an initial set of possible matches. Then two filtering steps are applied to the results to increase the precision by deleting the mismatched pairs. To improve the recall, a spatial matching process is applied to the remaining unmatched points. Finally, a two-stage hierarchical-global model-based pose estimation and tracking algorithm based on Particle Swarm Optimiation (PSO) is proposed to track the human upper body through consecutive frames. Given the pose and the foreground IPs in the previous frame and the matched points in the current frame, the proposed PSO-based pose estimation and tracking algorithm estimates the current pose hierarchically by minimizing the discrepancy between the hypothesized pose and the real matched observed points in the first stage. Then a global PSO is applied to the pose estimated by the first stage to do a consistency check and pose refinement

Inferring Human Pose and Motion from Images

As optical gesture recognition technology advances, touchless human computer interfaces of the future will soon become a reality. One particular technology, markerless motion capture, has gained a large amount of attention, with widespread application in diverse disciplines, including medical science, sports analysis, advanced user interfaces, and virtual arts. However, the complexity of human anatomy makes markerless motion capture a non-trivial problem: I) parameterised pose configuration exhibits high dimensionality, and II) there is considerable ambiguity in surjective inverse mapping from observation to pose configuration spaces with a limited number of camera views. These factors together lead to multimodality in high dimensional space, making markerless motion capture an ill-posed problem. This study challenges these difficulties by introducing a new framework. It begins with automatically modelling specific subject template models and calibrating posture at the initial stage. Subsequent tracking is accomplished by embedding naturally-inspired global optimisation into the sequential Bayesian filtering framework. Tracking is enhanced by several robust evaluation improvements. Sparsity of images is managed by compressive evaluation, further accelerating computational efficiency in high dimensional space

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Satellite Articulation Sensing using Computer Vision

Autonomous on-orbit satellite servicing benefits from an inspector satellite that can gain as much information as possible about the primary satellite. This includes performance of articulated objects such as solar arrays, antennas, and sensors. A method for building an articulated model from monocular imagery using tracked feature points and the known relative inspection route is developed. Two methods are also developed for tracking the articulation of a satellite in real-time given an articulated model using both tracked feature points and image silhouettes. Performance is evaluated for multiple inspection routes and the effect of inspection route noise is assessed. Additionally, a satellite model is built and used to collect stop-motion images simulating articulated motion over an inspection route under simulated space illumination. The images are used in the silhouette articulation tracking method and successful tracking is demonstrated qualitatively. Finally, a human pose tracking algorithm is modified for tracking the satellite articulation demonstrating the applicability of human tracking methods to satellite articulation tracking methods when an articulated model is available

Articulation estimation and real-time tracking of human hand motions

Schröder M. Articulation estimation and real-time tracking of human hand motions. Bielefeld: Universität Bielefeld; 2015.This thesis deals with the problem of estimating and tracking the full articulation of human hands. Algorithmically recovering hand articulations is a challenging problem due to the hand’s high number of degrees of freedom and the complexity of its motions. Besides the accuracy and efficiency of the hand posture estimation, hand tracking methods are faced with issues such as invasiveness, ease of deployment and sensor artifacts. In this thesis several different hand tracking approaches are examined, including marker-based optical motion capture, data-driven discriminative visual tracking and generative tracking based on articulated registration, and various contributions to these areas are presented. The problem of optimally placing reduced marker sets on a performer’s hand for optical hand motion capture is explored. A method is proposed that automatically generates functional reduced marker layouts by optimizing for their numerical stability and geometric feasibility. A data-driven discriminative tracking approach based on matching the hand’s appearance in the sensor data with an image database is investigated. In addition to an efficient nearest neighbor search for images, a combination of discriminative initialization and generative refinement is employed. The method’s applicability is demonstrated in interactive robot teleoperation. Various real human hand motions are captured and statistically analyzed to derive low-dimensional representations of hand articulations. An adaptive hand posture subspace concept is developed and integrated into a generative real-time hand tracking approach that aligns a virtual hand model with sensor point clouds based on constrained inverse kinematics. Generative hand tracking is formulated as a regularized articulated registration process, in which geometrical model fitting is combined with statistical, kinematic and temporal regularization priors. A registration concept that combines 2D and 3D alignment and explicitly accounts for occlusions and visibility constraints is devised. High-quality, non-invasive, real-time hand tracking is achieved based on this regularized articulated registration formulation

Swarm Robotics

Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties