Multi-View Region Adaptive Multi-temporal DMM and RGB Action Recognition

Human action recognition remains an important yet challenging task. This work proposes a novel action recognition system. It uses a novel Multiple View Region Adaptive Multi-resolution in time Depth Motion Map (MV-RAMDMM) formulation combined with appearance information. Multiple stream 3D Convolutional Neural Networks (CNNs) are trained on the different views and time resolutions of the region adaptive Depth Motion Maps. Multiple views are synthesised to enhance the view invariance. The region adaptive weights, based on localised motion, accentuate and differentiate parts of actions possessing faster motion. Dedicated 3D CNN streams for multi-time resolution appearance information (RGB) are also included. These help to identify and differentiate between small object interactions. A pre-trained 3D-CNN is used here with fine-tuning for each stream along with multiple class Support Vector Machines (SVM)s. Average score fusion is used on the output. The developed approach is capable of recognising both human action and human-object interaction. Three public domain datasets including: MSR 3D Action,Northwestern UCLA multi-view actions and MSR 3D daily activity are used to evaluate the proposed solution. The experimental results demonstrate the robustness of this approach compared with state-of-the-art algorithms.Comment: 14 pages, 6 figures, 13 tables. Submitte

Learning discriminative features for human motion understanding

Human motion understanding has attracted considerable interest in recent research for its applications to video surveillance, content-based search and healthcare. With different capturing methods, human motion can be recorded in various forms (e.g. skeletal data, video, image, etc.). Compared to the 2D video and image, skeletal data recorded by motion capture device contains full 3D movement information. To begin with, we first look into a gait motion analysis problem based on 3D skeletal data. We propose an automatic framework for identifying musculoskeletal and neurological disorders among older people based on 3D skeletal motion data. In this framework, a feature selection strategy and two new gait features are proposed to choose an optimal feature set from the input features to optimise classification accuracy. Due to self-occlusion caused by single shooting angle, 2D video and image are not able to record full 3D geometric information. Therefore, viewpoint variation dramatically affects the performance on lots of 2D based applications (e.g. arbitrary view action recognition and image-based 3D human shape reconstruction). Leveraging view-invariance from the 3D model is a popular idea to improve the performance on 2D computer vision problems. Therefore, in the second contribution, we adopt 3D models built with computer graphics technology to assist in solving the problem of arbitrary view action recognition. As a solution, a new transfer dictionary learning framework that utilises computer graphics technologies to synthesise realistic 2D and 3D training videos is proposed, which can project a real-world 2D video into a view-invariant sparse representation. In the third contribution, 3D models are utilised to build an end-to-end 3D human shape reconstruction system, which can recover the 3D human shape from a single image without any prior parametric model. In contrast to most existing methods that calculate 3D joint locations, the method proposed in this thesis can produce a richer and more useful point cloud based representation. Synthesised high-quality 2D images and dense 3D point clouds are used to train a CNN-based encoder and 3D regression module. It can be concluded that the methods introduced in this thesis try to explore human motion understanding from 3D to 2D. We investigate how to compensate for the lack of full geometric information in 2D based applications with view-invariance learnt from 3D models

Fast, invariant representation for human action in the visual system

Humans can effortlessly recognize others' actions in the presence of complex transformations, such as changes in viewpoint. Several studies have located the regions in the brain involved in invariant action recognition, however, the underlying neural computations remain poorly understood. We use magnetoencephalography (MEG) decoding and a dataset of well-controlled, naturalistic videos of five actions (run, walk, jump, eat, drink) performed by different actors at different viewpoints to study the computational steps used to recognize actions across complex transformations. In particular, we ask when the brain discounts changes in 3D viewpoint relative to when it initially discriminates between actions. We measure the latency difference between invariant and non-invariant action decoding when subjects view full videos as well as form-depleted and motion-depleted stimuli. Our results show no difference in decoding latency or temporal profile between invariant and non-invariant action recognition in full videos. However, when either form or motion information is removed from the stimulus set, we observe a decrease and delay in invariant action decoding. Our results suggest that the brain recognizes actions and builds invariance to complex transformations at the same time, and that both form and motion information are crucial for fast, invariant action recognition

Histogram of Oriented Principal Components for Cross-View Action Recognition

Existing techniques for 3D action recognition are sensitive to viewpoint variations because they extract features from depth images which are viewpoint dependent. In contrast, we directly process pointclouds for cross-view action recognition from unknown and unseen views. We propose the Histogram of Oriented Principal Components (HOPC) descriptor that is robust to noise, viewpoint, scale and action speed variations. At a 3D point, HOPC is computed by projecting the three scaled eigenvectors of the pointcloud within its local spatio-temporal support volume onto the vertices of a regular dodecahedron. HOPC is also used for the detection of Spatio-Temporal Keypoints (STK) in 3D pointcloud sequences so that view-invariant STK descriptors (or Local HOPC descriptors) at these key locations only are used for action recognition. We also propose a global descriptor computed from the normalized spatio-temporal distribution of STKs in 4-D, which we refer to as STK-D. We have evaluated the performance of our proposed descriptors against nine existing techniques on two cross-view and three single-view human action recognition datasets. The Experimental results show that our techniques provide significant improvement over state-of-the-art methods