Human action analysis has recently received growing interest from vision researchers. In this paper, we present a simple but effective approach for action recognition by combining multiple complementary features with Gaussian process classification. Since it is often insufficient for a single type of feature derived from action videos to characterize variations among different motions, we propose to combine two types of features for better action representation, namely the quantized vocabulary of spatio-temporal (ST) volumes and the quantized vocabulary of silhouette projection (SP) histograms, capturing the global and local motions as well as the appearances of the actions respectively. The use of such features also elegantly converts the original temporal classification problem into a static classification problem, which enables the use of any existing classifiers. Besides the traditional K-nearest neighbor (KNN), we use the state-of-the-art Gaussian process (GP) model for achieving stable performance. We also investigate how the number of features can be reduced while still maintaining classification accuracy. Our experimental results have shown that the fusion of multiple features improved the recognition accuracy compared with the use of any single feature type. We also show that the redundancy of fused features can be reduced by spectral feature analysis. Compared with KNN, the use of the GP model significantly improved the stability of the algorithm's performance with respect to the parameter settings.

We use spectral analysis to facilitate Gaussian processes (GP) classification. Our solution provides two improvements: scaling of the data to achieve a more isotropic nature, as well as a method to choose the kernel to match certain data characteristics. Given the dataset, from the Fourier transform of the training data we compare the frequency domain features of each dimension to estimate a rescaling (towards making the data isotropic). Also, the spectrum of the training data is compared with several candidate kernel spectrums. From this comparison the best matching kernel is chosen. In these ways, the training data matches better the GP classification kernel function (and hence the underlying assumed correlation characteristics), resulting in a better GP classification result. Test results on both non image and image data show the efficiency and effectiveness of our approach.

We improve Gaussian processes (GP) classification by reorganizing the (non-stationary and anisotropic) data to better fit to the isotropic GP kernel. First, the data is partitioned into two parts: along the feature with the highest frequency bandwidth. Secondly, for each part of the data, only the spectrally homogeneous features are chosen and used (the rest discarded) for GP classification. In this way, anisotropy of the data is lessened from the frequency point of view. Tests on synthetic data as well as real datasets show that our approach is effective and outperforms Automatic Relevance Determination (ARD).

This paper investigates the applicability of Gaussian Processes (GP) classification for recognition of articulated and deformable human motions from image sequences. Using Tensor Subspace Analysis (TSA), space-time human silhouettes (extracted from motion videos) are transformed to low-dimensional multivariate time series, based on which structure-based statistical features are calculated to summarize the motion properties. GP classification is then used to learn and predict motion categories. Experimental results on two real-world state-of-the-art datasets show that the proposed approach is effective, and outperforms Support Vector Machine (SVM).

Informative Vector Machine (IVM) is an efficient fast sparse Gaussian process's (GP) method previously suggested for active learning. It greatly reduces the computational cost of GP classification and makes the GP learning close to real time. We apply IVM for man-made structure classification (a two class problem). Our work includes the investigation of the performance of IVM with varied active data points as well as the effects of different choices of GP kernels. Satisfactory results have been obtained, showing that the approach keeps full GP classification performance and yet is significantly faster (by virtue if using a subset of the whole training data points).

We apply Gaussian process classification (GPC) to man-made structure segmentation, treated as a two class problem. GPC is a discriminative approach, and thus focuses on modelling the posterior directly. It relaxes the strong assumption of conditional independence of the observed data (generally used in a generative model). In addition, wavelet transform features, which are effective in describing directional textures, are incorporated in the feature vector. Satisfactory results have been obtained which show the effectiveness of our approach.

Wireless video surveillance over CDMA network enables transfer of high quality live video via public wireless broadband, which is accessible anywhere with CDMA mobile phone network coverage. Real time video can be received on an internet connected PC, laptop or even mobile phone. This makes possible surveillance from moving vehicles, or quick deployment in almost unrestricted locations. While many general purpose solutions exist for sending video over CDMA, they have not taken into consideration certain specific features of the network nor the requirements of the related applications. This paper presents a new architecture for video transmission over CDMA with simple hardware complexity, high reliability and rich functions. The compact and flexible system has been under trial in China, Australia, Singapore, Italy, Egypt and Brazil, amongst others.