73 research outputs found

    Posing 3D Models from Drawing

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    Inferring the 3D pose of a character from a drawing is a complex and under-constrained problem. Solving it may help automate various parts of an animation production pipeline such as pre-visualisation. In this paper, a novel way of inferring the 3D pose from a monocular 2D sketch is proposed. The proposed method does not make any external assumptions about the model, allowing it to be used on different types of characters. The inference of the 3D pose is formulated as an optimisation problem and a parallel variation of the Particle Swarm Optimisation algorithm called PARAC-LOAPSO is utilised for searching the minimum. Testing in isolation as well as part of a larger scene, the presented method is evaluated by posing a lamp, a horse and a human character. The results show that this method is robust, highly scalable and is able to be extended to various types of models

    Comparing Evolutionary Algorithms and Particle Filters for Markerless Human Motion Capture

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    Markerless Human Motion Capture is the problem of determining the joints’ angles of a three-dimensional articulated body model that best matches current and past observations acquired by video cameras. The problem of Markerless Human Motion Capture is high-dimensional and requires the use of models with a considerable number of degrees of freedom to appropriately adapt to the human anatomy. Particle filters have become the most popular approach for Markerless Human Motion Capture, despite their difficulty to cope with high-dimensional problems. Although several solutions have been proposed to improve their performance, they still suffer from the curse of dimensionality. As a consequence, it is normally required to impose mobility limitations in the body models employed, or to exploit the hierarchical nature of the human skeleton by partitioning the problem into smaller ones. Evolutionary algorithms, though, are powerful methods for solving continuous optimization problems, specially the high-dimensional ones. Yet, few works have tackled Markerless Human Motion Capture using them. This paper evaluates the performance of three of the most competitive algorithms in continuous optimization – Covariance Matrix Adaptation Evolutionary Strategy, Differential Evolution and Particle Swarm Optimization – with two of the most relevant particle filters proposed in the literature, namely the Annealed Particle Filter and the Partitioned Sampling Annealed Particle Filter. The algorithms have been experimentally compared in the public dataset HumanEva-I by employing two body models with different complexities. Our work also analyzes the performance of the algorithms in hierarchical and holistic approaches, i.e., with and without partitioning the search space. Non-parametric tests run on the results have shown that: (i) the evolutionary algorithms employed outperform their particle filter counterparts in all the cases tested; (ii) they can deal with high-dimensional models thus leading to better accuracy; and (iii) the hierarchical strategy surpasses the holistic one

    Inferring Human Pose and Motion from Images

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    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

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

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    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

    Tracking human motion with multiple cameras using articulated ICP with hard constraints

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    Questa tesi propone un nuovo algoritmo basato su ICP per il tracking di un modello scheletrico articolato di un corpo umano. L\u2019algoritmo proposto prende in input immagini calibrate di un soggetto, calcola la ricostruzione volumetrica e la linea mediale del corpo e quindi posiziona in modo adeguato il modello, composto di segmenti, in ogni frame usando una versione di ICP modificata (versione che usa una strategia di attraversamento alberi gerarchica che mantiene connessi tutti i segmenti del modello nei giunti relativi). L\u2019approccio proposto usa limiti cinematica per i giunti e un filtro di Kalman esteso per fare il tracking del modello. Il primo contributo originale di questa tesi \ue8 l\u2019algoritmo per trovare i punti sullo scheletro di un volume tridimensionale. L\u2019algoritmo, usando una tecnica di slicing trova l\u2019asse mediale di un volume 3D in modo veloce utilizzando il processore della scheda grafica e le texture units della scheda stessa. Questo algoritmo produce ottimi risultati per quanto riguarda la qualit\ue0 e le prestazioni se comparato con altri algoritmi in letteratura. Un altro contributo originale \ue8 l\u2019introduzione di una nuova strategia di tracking basata su un approccio gerarchico dell\u2019algoritmo ICP, utilizzato per trovare le congruenze tra un modello di corpo umano composto da soli segmenti e un insieme di punti 3D. L\u2019algoritmo usa una versione di ICP dove tutti i punti 3D sono pesati in funzione del segmento del corpo preso in considerazione dall\u2019algoritmo in quel momento. L\u2019applicazione di queste tecniche dimostra la bont\ue0 del metodo e le prestazioni ottenute in termini di qualit\ue0 della stima della posa sono comparabili con altri lavori in letteratura. I risultati presentati nella tesi dimostrano la fattibilit\ue0 dell\u2019approccio generale, che si intende utilizzare in un sistema completo per il tracking di corpi umani senza l\u2019uso di marcatori. In futuro il lavoro pu\uf2 essere esteso ottimizzando l\u2019implementazione e la codifica in modo da poter ottenere prestazioni real-time.This thesis proposed a new ICP-based algorithm for tracking articulated skeletal model of a human body. The proposed algorithm takes as input multiple calibrated views of the subject, computes a volumetric reconstruction and the centerlines of the body and fits the skeletal body model in each frame using a hierarchic tree traversal version of the ICP algorithm that preserves the connection of the segments at the joints. The proposed approach uses the kinematic constraints and an Extended Kalman Filter to track the body pose. The first contribution is a new algorithm to find the skeletal points of a 3D volume. The algorithm using a slicing technique find the medial axis of a volume in a fast way using the graphic card processor and the texture units. This algorithm produce good results in quality and performance compared to other works in literature. Another contribution is the introduction of a new tracking strategy based on a hierarchical application of the ICP standard algorithm to find the match between a stick body model and a set of 3D points. The algorithm use a traversing version of ICP where also all the 3D points are weighted in such a way every limbs of the model can best fit on the right portion of the body. The application of these techniques shown the feasibility of the method and the performances obtained in terms of quality of estimate pose are comparable with other works in literature. The results presented here demonstrate the feasibility of the approach, which is is intended to be used in complete system for vision-based markerless human body tracking. Future work will aimed at optimizing the implementation, in order to achieve real-time performances

    Metaheuristic Optimization Techniques for Articulated Human Tracking

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    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
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