6 research outputs found
Comparing Evolutionary Algorithms and Particle Filters for Markerless Human Motion Capture
Markerless Human Motion Capture is the problem of determining the jointsâ 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 â Covariance Matrix Adaptation Evolutionary Strategy, Differential Evolution and Particle Swarm Optimization â 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
Parallelization Strategies for Markerless Human Motion Capture
Markerless Motion Capture (MMOCAP) is the
problem of determining the pose of a person from images
captured by one or several cameras simultaneously without
using markers on the subject. Evaluation of the solutions
is frequently the most time-consuming task, making most
of the proposed methods inapplicable in real-time scenarios.
This paper presents an efficient approach to parallelize
the evaluation of the solutions in CPUs and GPUs. Our proposal
is experimentally compared on six sequences of the
HumanEva-I dataset using the CMAES algorithm. Multiple
algorithmâs configurations were tested to analyze the
best trade-off in regard to the accuracy and computing time.
The proposed methods obtain speedups of 8Ă in multi-core
CPUs, 30Ă in a single GPU and up to 110Ă using 4 GPU
Human interaction categorization by using audio-visual cues
Human Interaction Recognition (HIR) in uncontrolled TV video material is a very challenging problem because of the huge intra-class variability of the classes (due to large differences in the way actions are performed, lighting conditions and camera viewpoints, amongst others) as well as the existing small inter-class variability (e.g., the visual difference between hug and kiss is very subtle). Most of previous works have been focused only on visual information (i.e., image signal), thus missing an important source of information present in human interactions: the audio. So far, such approaches have not shown to be discriminative enough. This work proposes the use of Audio-Visual Bag of Words (AVBOW) as a more powerful mechanism to approach the HIR problem than the traditional Visual Bag of Words (VBOW). We show in this paper that the combined use of video and audio information yields to better classification results than video alone. Our approach has been validated in the challenging TVHID dataset showing that the proposed AVBOW provides statistically significant improvements over the VBOW employed in the related literature