Spatio-temporal human action detection and instance segmentation in videos

With an exponential growth in the number of video capturing devices and digital video content, automatic video understanding is now at the forefront of computer vision research. This thesis presents a series of models for automatic human action detection in videos and also addresses the space-time action instance segmentation problem. Both action detection and instance segmentation play vital roles in video understanding. Firstly, we propose a novel human action detection approach based on a frame-level deep feature representation combined with a two-pass dynamic programming approach. The method obtains a frame-level action representation by leveraging recent advances in deep learning based action recognition and object detection methods. To combine the the complementary appearance and motion cues, we introduce a new fusion technique which signicantly improves the detection performance. Further, we cast the temporal action detection as two energy optimisation problems which are solved using Viterbi algorithm. Exploiting a video-level representation further allows the network to learn the inter-frame temporal correspondence between action regions and it is bound to be a more optimal solution to the action detection problem than a frame-level representation. Secondly, we propose a novel deep network architecture which learns a video-level action representation by classifying and regressing 3D region proposals spanning two successive video frames. The proposed model is end-to-end trainable and can be jointly optimised for both proposal generation and action detection objectives in a single training step. We name our new network as \AMTnet" (Action Micro-Tube regression Network). We further extend the AMTnet model by incorporating optical ow features to encode motion patterns of actions. Finally, we address the problem of action instance segmentation in which multiple concurrent actions of the same class may be segmented out of an image sequence. By taking advantage of recent work on action foreground-background segmentation, we are able to associate each action tube with class-specic segmentations. We demonstrate the performance of our proposed models on challenging action detection benchmarks achieving new state-of-the-art results across the board and signicantly increasing detection speed at test time

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

Human Motion Anticipation and Recognition from RGB-D

Predicting and understanding the dynamic of human motion has many applications such as motion synthesis, augmented reality, security, education, reinforcement learning, autonomous vehicles, and many others. In this thesis, we create a novel end-to-end pipeline that can predict multiple future poses from the same input, and, in addition, can classify the entire sequence. Our focus is on the following two aspects of human motion understanding: Probabilistic human action prediction: Given a sequence of human poses as input, we sample multiple possible future poses from the same input sequence using a new GAN-based network. Human motion understanding: Given a sequence of human poses as input, we classify the actual action performed in the sequence and improve the classification performance using the presentation learned from the prediction network. We also demonstrate how to improve model training from noisy labels, using facial expression recognition as an example. More specifically, we have 10 taggers to label each input image, and compare four different approaches: majority voting, multi-label learning, probabilistic label drawing, and cross-entropy loss. We show that the traditional majority voting scheme does not perform as well as the last two approaches that fully leverage the label distribution. We shared the enhanced FER+ data set with multiple labels for each face image with the research community (https://github.com/Microsoft/FERPlus). For predicting and understanding of human motion, we propose a novel sequence-to-sequence model trained with an improved version of generative adversarial networks (GAN). Our model, which we call HP-GAN2, learns a probability density function of future human poses conditioned on previous poses. It predicts multiple sequences of possible future human poses, each from the same input sequence but seeded with a different vector z drawn from a random distribution. Moreover, to quantify the quality of the non-deterministic predictions, we simultaneously train a motion-quality-assessment model that learns the probability that a given skeleton pose sequence is a real or fake human motion. In order to classify the action performed in a video clip, we took two approaches. In the first approach, we train on a sequence of skeleton poses from scratch using random parameters initialization with the same network architecture used in the discriminator of the HP-GAN2 model. For the second approach, we use the discriminator of the HP-GAN2 network, extend it with an action classification branch, and fine tune the end-to-end model on the classification tasks, since the discriminator in HP-GAN2 learned to differentiate between fake and real human motion. So, our hypothesis is that if the discriminator network can differentiate between synthetic and real skeleton poses, then it also has learned some of the dynamics of a real human motion, and that those dynamics are useful in classification as well. We will show through multiple experiments that that is indeed the case. Therefore, our model learns to predict multiple future sequences of human poses from the same input sequence. We also show that the discriminator learns a general representation of human motion by using the learned features in an action recognition task. And we train a motion-quality-assessment network that measure the probability of a given sequence of poses are valid human poses or not. We test our model on two of the largest human pose datasets: NTURGB-D, and Human3.6M. We train on both single and multiple action types. The predictive power of our model for motion estimation is demonstrated by generating multiple plausible futures from the same input and showing the effect of each of the several loss functions in the ablation study. We also show the advantage of switching to GAN from WGAN-GP, which we used in our previous work. Furthermore, we show that it takes less than half the number of epochs to train an activity recognition network by using the features learned from the discriminator

Hidden Two-Stream Convolutional Networks for Action Recognition

Analyzing videos of human actions involves understanding the temporal relationships among video frames. State-of-the-art action recognition approaches rely on traditional optical flow estimation methods to pre-compute motion information for CNNs. Such a two-stage approach is computationally expensive, storage demanding, and not end-to-end trainable. In this paper, we present a novel CNN architecture that implicitly captures motion information between adjacent frames. We name our approach hidden two-stream CNNs because it only takes raw video frames as input and directly predicts action classes without explicitly computing optical flow. Our end-to-end approach is 10x faster than its two-stage baseline. Experimental results on four challenging action recognition datasets: UCF101, HMDB51, THUMOS14 and ActivityNet v1.2 show that our approach significantly outperforms the previous best real-time approaches.Comment: Accepted at ACCV 2018, camera ready. Code available at https://github.com/bryanyzhu/Hidden-Two-Strea

Discriminatively Trained Latent Ordinal Model for Video Classification

We study the problem of video classification for facial analysis and human action recognition. We propose a novel weakly supervised learning method that models the video as a sequence of automatically mined, discriminative sub-events (eg. onset and offset phase for "smile", running and jumping for "highjump"). The proposed model is inspired by the recent works on Multiple Instance Learning and latent SVM/HCRF -- it extends such frameworks to model the ordinal aspect in the videos, approximately. We obtain consistent improvements over relevant competitive baselines on four challenging and publicly available video based facial analysis datasets for prediction of expression, clinical pain and intent in dyadic conversations and on three challenging human action datasets. We also validate the method with qualitative results and show that they largely support the intuitions behind the method.Comment: Paper accepted in IEEE TPAMI. arXiv admin note: substantial text overlap with arXiv:1604.0150

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

Time-Contrastive Networks: Self-Supervised Learning from Video

We propose a self-supervised approach for learning representations and robotic behaviors entirely from unlabeled videos recorded from multiple viewpoints, and study how this representation can be used in two robotic imitation settings: imitating object interactions from videos of humans, and imitating human poses. Imitation of human behavior requires a viewpoint-invariant representation that captures the relationships between end-effectors (hands or robot grippers) and the environment, object attributes, and body pose. We train our representations using a metric learning loss, where multiple simultaneous viewpoints of the same observation are attracted in the embedding space, while being repelled from temporal neighbors which are often visually similar but functionally different. In other words, the model simultaneously learns to recognize what is common between different-looking images, and what is different between similar-looking images. This signal causes our model to discover attributes that do not change across viewpoint, but do change across time, while ignoring nuisance variables such as occlusions, motion blur, lighting and background. We demonstrate that this representation can be used by a robot to directly mimic human poses without an explicit correspondence, and that it can be used as a reward function within a reinforcement learning algorithm. While representations are learned from an unlabeled collection of task-related videos, robot behaviors such as pouring are learned by watching a single 3rd-person demonstration by a human. Reward functions obtained by following the human demonstrations under the learned representation enable efficient reinforcement learning that is practical for real-world robotic systems. Video results, open-source code and dataset are available at https://sermanet.github.io/imitat