Eigenvector-based Dimensionality Reduction for Human Activity Recognition and Data Classification

In the context of appearance-based human motion compression, representation, and recognition, we have proposed a robust framework based on the eigenspace technique. First, the new appearance-based template matching approach which we named Motion Intensity Image for compressing a human motion video into a simple and concise, yet very expressive representation. Second, a learning strategy based on the eigenspace technique is employed for dimensionality reduction using each of PCA and FDA, while providing maximum data variance and maximum class separability, respectively. Third, a new compound eigenspace is introduced for multiple directed motion recognition that takes care also of the possible changes in scale. This method extracts two more features that are used to control the recognition process. A similarity measure, based on Euclidean distance, has been employed for matching dimensionally-reduced testing templates against a projected set of known motions templates. In the stream of nonlinear classification, we have introduced a new eigenvector-based recognition model, built upon the idea of the kernel technique. A practical study on the use of the kernel technique with 18 different functions has been carried out. We have shown in this study how crucial choosing the right kernel function is, for the success of the subsequent linear discrimination in the feature space for a particular problem. Second, building upon the theory of reproducing kernels, we have proposed a new robust nonparametric discriminant analysis approach with kernels. Our proposed technique can efficiently find a nonparametric kernel representation where linear discriminants can perform better. Data classification is achieved by integrating the linear version of the NDA with the kernel mapping. Based on the kernel trick, we have provided a new formulation for Fisher\u27s criterion, defined in terms of the Gram matrix only

NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding

Research on depth-based human activity analysis achieved outstanding performance and demonstrated the effectiveness of 3D representation for action recognition. The existing depth-based and RGB+D-based action recognition benchmarks have a number of limitations, including the lack of large-scale training samples, realistic number of distinct class categories, diversity in camera views, varied environmental conditions, and variety of human subjects. In this work, we introduce a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames. This dataset contains 120 different action classes including daily, mutual, and health-related activities. We evaluate the performance of a series of existing 3D activity analysis methods on this dataset, and show the advantage of applying deep learning methods for 3D-based human action recognition. Furthermore, we investigate a novel one-shot 3D activity recognition problem on our dataset, and a simple yet effective Action-Part Semantic Relevance-aware (APSR) framework is proposed for this task, which yields promising results for recognition of the novel action classes. We believe the introduction of this large-scale dataset will enable the community to apply, adapt, and develop various data-hungry learning techniques for depth-based and RGB+D-based human activity understanding. [The dataset is available at: http://rose1.ntu.edu.sg/Datasets/actionRecognition.asp]Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI

Visual Dynamics: Stochastic Future Generation via Layered Cross Convolutional Networks

We study the problem of synthesizing a number of likely future frames from a single input image. In contrast to traditional methods that have tackled this problem in a deterministic or non-parametric way, we propose to model future frames in a probabilistic manner. Our probabilistic model makes it possible for us to sample and synthesize many possible future frames from a single input image. To synthesize realistic movement of objects, we propose a novel network structure, namely a Cross Convolutional Network; this network encodes image and motion information as feature maps and convolutional kernels, respectively. In experiments, our model performs well on synthetic data, such as 2D shapes and animated game sprites, and on real-world video frames. We present analyses of the learned network representations, showing it is implicitly learning a compact encoding of object appearance and motion. We also demonstrate a few of its applications, including visual analogy-making and video extrapolation.Comment: Journal preprint of arXiv:1607.02586 (IEEE TPAMI, 2019). The first two authors contributed equally to this work. Project page: http://visualdynamics.csail.mit.ed

Going Deeper into Action Recognition: A Survey

Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation and semantic segmentation. Over the last decade, human action analysis evolved from earlier schemes that are often limited to controlled environments to nowadays advanced solutions that can learn from millions of videos and apply to almost all daily activities. Given the broad range of applications from video surveillance to human-computer interaction, scientific milestones in action recognition are achieved more rapidly, eventually leading to the demise of what used to be good in a short time. This motivated us to provide a comprehensive review of the notable steps taken towards recognizing human actions. To this end, we start our discussion with the pioneering methods that use handcrafted representations, and then, navigate into the realm of deep learning based approaches. We aim to remain objective throughout this survey, touching upon encouraging improvements as well as inevitable fallbacks, in the hope of raising fresh questions and motivating new research directions for the reader