Recognition of human periodic motion: a frequency domain approach

We present a frequency domain analysis technique for modelling and recognizing human periodic movements from moving light displays (MLDs). We model periodic motions by motion templates, that consist of a set of feature power vectors extracted from unidentified vertical component trajectories of feature points. Motion recognition is carried out in the frequency domain, by comparing an observed motion template with pre-stored templates. This method contrasts with common spatio-temporal approaches. The proposed method is demonstrated by some examples of human periodic motion recognition in MLDs

Dynamic segment-based sparse feature-point matching in articulate motion

We propose an algorithm for identifying articulated motion. The motion is represented by a sequence of 3D sparse feature-point data. The algorithm emphasizes a self-initializing identification phase for each uninterrupted data sequence, typically at the beginning or on resumption of tracking. We combine a dynamic segment-based hierarchial identification with a inter-frame tracking strategy for efficiency and robustness. We have tested the algorithm successfully using human motion data obtained from a marker-based optical motion capture (MoCap) system

Parameterization of point-cloud freeform surfaces using adaptive sequential learning RBFnetworks

We propose a self-organizing Radial Basis Function (RBF) neural network method for parameterization of freeform surfaces from larger, noisy and unoriented point clouds. In particular, an adaptive sequential learning algorithm is presented for network construction from a single instance of point set. The adaptive learning allows neurons to be dynamically inserted and fully adjusted (e.g. their locations, widths and weights), according to mapping residuals and data point novelty associated to underlying geometry. Pseudo-neurons, exhibiting very limited contributions, can be removed through a pruning procedure. Additionally, a neighborhood extended Kalman filter (NEKF) was developed to significantly accelerate parameterization. Experimental results show that this adaptive learning enables effective capture of global low-frequency variations while preserving sharp local details, ultimately leading to accurate and compact parameterization, as characterized by a small number of neurons. Parameterization using the proposed RBF network provides simple, low cost and low storage solutions to many problems such as surface construction, re-sampling, hole filling, multiple level-of-detail meshing and data compression from unstructured and incomplete range data. Performance results are also presented for comparison

Adaptive point-cloud surface interpretation

We present a novel adaptive radial basis function network to reconstruct smooth closed surfaces and complete meshes from nonuniformly sampled noisy range data. The network is established using a heuristic learning strategy. Neurons can be inserted, removed or updated iteratively, adapting to the complexity and distribution of the underlying data. This flexibility is particularly suited to highly variable spatial frequencies, and is conducive to data compression with network representations. In addition, a greedy neighbourhood Extended Kalman Filter learning method is investigated, leading to a significant reduction of computational cost in the training process with desired prediction accuracy. Experimental results demonstrate the performance advantages of compact network representation for surface reconstruction from large amount of non-uniformly sampled incomplete point-clouds

Articulated pose identification with sparse point features

We propose a general algorithm for identifying an arbitrary pose of an articulated subject with sparse point features. The algorithm aims to identify a one-to-one correspondence between a model point-set and an observed point-set taken from freeform motion of the articulated subject. We avoid common assumptions such as pose similarity or small motions with respect to the model, and assume no prior knowledge from which to infer an initial or partial correspondence between the two point-sets. The algorithm integrates local segment-based correspondences under a set of affine transformations, and a global hierarchical search strategy. Experimental results, based on synthetic pose and real-world human motion data demonstrate the ability of the algorithm to perform the identification task. Reliability is increasingly compromised with increasing data noise and segmental distortion, but the algorithm can tolerate moderate levels. This work contributes to establishing a crucial self-initializing identification in model-based point-feature tracking for articulated motion

Articulated point pattern matching in optical motion capture systems

Tracking and identifying articulated objects have received growing attention in computer vision in the past decade. In marker-based optical motion capture (MoCap) systems, an articulated movement of near-rigid segments is represented via a sequence of moving dots of known 3D coordinates, corresponding to the captured marker positions. We propose a segment-based articulated model-fitting algorithm to address the problem of self-initializing identification and pose estimation utilizing one frame of data in such point-feature tracking systems. It is ultimately crucial for recovering the complete motion sequence. Experimental results, based on synthetic pose and real-world human motion capture data, demonstrate the performance of the algorithm

Functional modelling of large scattered data sets using neural networks

We propose a self-organising hierarchical Radial Basis Function (RBF) network for functional modelling of large amounts of scattered unstructured point data. The network employs an error-driven active learning algorithm and a multi-layer architecture, allowing progressive bottom-up reinforcement of local features in subdivisions of error clusters. For each RBF subnet, neurons can be inserted, removed or updated iteratively with full dimensionality adapting to the complexity and distribution of the underlying data. This flexibility is particularly desirable for highly variable spatial frequencies. Experimental results demonstrate that the network representation is conducive to geometric data formulation and simplification, and therefore to manageable computation and compact storage