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

When Kernel Methods meet Feature Learning: Log-Covariance Network for Action Recognition from Skeletal Data

Human action recognition from skeletal data is a hot research topic and important in many open domain applications of computer vision, thanks to recently introduced 3D sensors. In the literature, naive methods simply transfer off-the-shelf techniques from video to the skeletal representation. However, the current state-of-the-art is contended between to different paradigms: kernel-based methods and feature learning with (recurrent) neural networks. Both approaches show strong performances, yet they exhibit heavy, but complementary, drawbacks. Motivated by this fact, our work aims at combining together the best of the two paradigms, by proposing an approach where a shallow network is fed with a covariance representation. Our intuition is that, as long as the dynamics is effectively modeled, there is no need for the classification network to be deep nor recurrent in order to score favorably. We validate this hypothesis in a broad experimental analysis over 6 publicly available datasets.Comment: 2017 IEEE Computer Vision and Pattern Recognition (CVPR) Workshop

Learning Human Poses from Monocular Images

In this research, we mainly focus on the problem of estimating the 2D human pose from a monocular image and reconstructing the 3D human pose based on the 2D human pose. Here a 3D pose is the locations of the human joints in the 3D space and a 2D pose is the projection of a 3D pose on an image. Unlike many previous works that explicitly use hand-crafted physiological models, both our 2D pose estimation and 3D pose reconstruction approaches implicitly learn the structure of human body from human pose data. This 3D pose reconstruction is an ill-posed problem without considering any prior knowledge. In this research, we propose a new approach, namely Pose Locality Constrained Representation (PLCR), to constrain the search space for the underlying 3D human pose and use it to improve 3D human pose reconstruction. In this approach, an over-complete pose dictionary is constructed by hierarchically clustering the 3D pose space into many subspaces. Then PLCR utilizes the structure of the over-complete dictionary to constrain the 3D pose solution to a set of highly-related subspaces. Finally, PLCR is combined into the matching-pursuit based algorithm for 3D human-pose reconstruction. The 2D human pose used in 3D pose reconstruction can be manually annotated or automatically estimated from a single image. In this research, we develop a new learning-based 2D human pose estimation approach based on a Dual-Source Deep Convolutional Neural Networks (DS-CNN). The proposed DS-CNN model learns the appearance of each local body part and the relations between parts simultaneously, while most of existing approaches consider them as two separate steps. In our experiments, the proposed DS-CNN model produces superior or comparable performance against the state-of-the-art 2D human-pose estimation approaches based on pose priors learned from hand-crafted models or holistic perspectives. Finally, we use our 2D human pose estimation approach to recognize human attributes by utilizing the strong correspondence between human attributes and human body parts. Then we probe if and when the CNN can find such correspondence by itself on human attribute recognition and bird species recognition. We find that there is direct correlation between the recognition accuracy and the correctness of the correspondence that the CNN finds