Dynamic Image Sequence Representation And Classification With Application To Human Motion Analysis

Dynamic image sequence is composed of a series of image frames with comparatively given order. Besides the spatial characteristic as a single image, dynamic image sequence also possesses of temporal characteristic, which is motion information. Dynamic image sequence modeling analyses and identifies the movement patterns of the sequence and describes them with natural language. It is one of the most promising research topics of computer vision and has important application on smart surveillance, man-machine interactive, motion analysis and so on.Effective detecting and describing the dynamic information in the image sequence is the core issue of dynamic image sequence modeling. Time series analysis methods are perfect statistical tools for analyzing the time series. However, many problems appear when applying them to modeling dynamic image sequence. Based on the theory of graphical model, especially the special graphical model for time sequence - Dynamic Bayesian Networks, we discuss the problems arising during the application of the two most popular time series models (hidden Markov model and state-space model) to the dynamic image sequence representation and improve the learning method of the models. We also bring forward a new time series model which is more suitable for the image sequences and evaluate our work in human movement analysis.In order to complement the deficiency of single feature and describe the image sequence more comprehensive, exact and credible, we introduce the factorial hidden Markov model as a feature-level fusion method and the parallel hidden Markov model as a decision-level fusion method. According to the experimental results and the correlation between features, we analyse the factors impacting the fusion performance in depth. When choosing features with small performance diversity and good performance, the two fusion algorithms improve the recognition performance efficiently. Basing on this foundation, the lower is the features'relativity, the better.Because dynamic texture model is not applicable to represent binary image sequence, we propose two improved dynamic texture models. The first is the binary dynamic texture model, which considers that binary image submits to Bernoulli distribution and adopts binary principal component analysis to learn the model parameters. The second, tensor subspace dynamic texture model, employs tensor subspace analysis to transform binary image sequence to low dimensional gray image sequence and then use dynamic texture to describe it. The experimental results show that the two improved models can describe the image sequence more exactly.Hidden Markov model and its extension have great limitation on choosing the hidden states exactly. Improved dynamic texture models are linear and have difficulties in describing image sequence comprehensively. We present a layered time series model to resolve the aforementioned deficiencies. The proposed model is a two-level statistical model. In the first level, we employ segmental linearity to approximate nonlinearity. The image sequence is divided into several clusters and each cluster is described by the dynamic texture model or improved dynamic texture model. In the second level, these models are considered as the hidden states and the hidden Markov model is built to characterize the relationship among them. The observation probability of the hidden Markov model is a function of the distance between the observation and the corresponding synthesized observation of these models. The combined parameters of the two levels are the parameters of the hierarchical time series model. The experiments results show that this model overcomes the limitation of hidden Markov model and dynamic texture model and remains the synthesis and prediction ability of the dynamic texture model and the description capability of hidden Markov model for dynamic process. It is an excellent model for describing and identifying the image sequences.We propose a new representation method named frame difference energy image to depress the influence of silhouette incompleteness caused by background subtraction in the gait database. This representation preserves the static information of each cluster and the positive portion of the frame difference between the former frame and the current frame. Combining static and dynamic information, not only does this method efficiently make up the deficiency of missing image information, but also embodies the change of human shape during walking and improves the recognition rate.In addition, the McNemar's test is introduced to evaluate different algorithms. This method needs many experimental results and a first order check on the statistical significance of an observed difference between algorithms is calculated to quantitatively compare their performance. Compared with other methods, this method is more accurate and credible for algorithm evaluation.