Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation

This paper proposes a new hybrid architecture that consists of a deep Convolutional Network and a Markov Random Field. We show how this architecture is successfully applied to the challenging problem of articulated human pose estimation in monocular images. The architecture can exploit structural domain constraints such as geometric relationships between body joint locations. We show that joint training of these two model paradigms improves performance and allows us to significantly outperform existing state-of-the-art techniques

Pose Embeddings: A Deep Architecture for Learning to Match Human Poses

We present a method for learning an embedding that places images of humans in similar poses nearby. This embedding can be used as a direct method of comparing images based on human pose, avoiding potential challenges of estimating body joint positions. Pose embedding learning is formulated under a triplet-based distance criterion. A deep architecture is used to allow learning of a representation capable of making distinctions between different poses. Experiments on human pose matching and retrieval from video data demonstrate the potential of the method

Markerless Motion Capture in the Crowd

This work uses crowdsourcing to obtain motion capture data from video recordings. The data is obtained by information workers who click repeatedly to indicate body configurations in the frames of a video, resulting in a model of 2D structure over time. We discuss techniques to optimize the tracking task and strategies for maximizing accuracy and efficiency. We show visualizations of a variety of motions captured with our pipeline then apply reconstruction techniques to derive 3D structure.Comment: Presented at Collective Intelligence conference, 2012 (arXiv:1204.2991

MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation

In this work, we propose a novel and efficient method for articulated human pose estimation in videos using a convolutional network architecture, which incorporates both color and motion features. We propose a new human body pose dataset, FLIC-motion, that extends the FLIC dataset with additional motion features. We apply our architecture to this dataset and report significantly better performance than current state-of-the-art pose detection systems

Learning Human Pose Estimation Features with Convolutional Networks

This paper introduces a new architecture for human pose estimation using a multi- layer convolutional network architecture and a modified learning technique that learns low-level features and higher-level weak spatial models. Unconstrained human pose estimation is one of the hardest problems in computer vision, and our new architecture and learning schema shows significant improvement over the current state-of-the-art results. The main contribution of this paper is showing, for the first time, that a specific variation of deep learning is able to outperform all existing traditional architectures on this task. The paper also discusses several lessons learned while researching alternatives, most notably, that it is possible to learn strong low-level feature detectors on features that might even just cover a few pixels in the image. Higher-level spatial models improve somewhat the overall result, but to a much lesser extent then expected. Many researchers previously argued that the kinematic structure and top-down information is crucial for this domain, but with our purely bottom up, and weak spatial model, we could improve other more complicated architectures that currently produce the best results. This mirrors what many other researchers, like those in the speech recognition, object recognition, and other domains have experienced

DeepPose: Human Pose Estimation via Deep Neural Networks

We propose a method for human pose estimation based on Deep Neural Networks (DNNs). The pose estimation is formulated as a DNN-based regression problem towards body joints. We present a cascade of such DNN regressors which results in high precision pose estimates. The approach has the advantage of reasoning about pose in a holistic fashion and has a simple but yet powerful formulation which capitalizes on recent advances in Deep Learning. We present a detailed empirical analysis with state-of-art or better performance on four academic benchmarks of diverse real-world images.Comment: IEEE Conference on Computer Vision and Pattern Recognition, 201

Flow Lookup and Biological Motion Perception

Optical flow in monocular video can serve as a key for recognizing and tracking the three-dimensional pose of human subjects. In comparison with prior work using silhouettes as a key for pose lookup, flow data contains richer information and in experiments can successfully track more difficult sequences. Furthermore, flow recognition is powerful enough to model human abilities in perceiving biological motion from sparse input. The experiments described herein show that a tracker using flow moment lookup can reconstruct a common biological motion (walking) from images containing only point light sources attached to the joints of the moving subject

Automatic detection of child pornography

Before the introduction of the internet, the availability of child pornography was reported as on the decline (Jenkins 2001). Since its emergence, however, the internet has made child pornography a much more accessible and available means of trafficking across borders (Biegel 2001; Jenkins 2001; Wells, Finkelhor et al. 2007). The internet as it is at present is made up of a vast array of protocols and networks where traffickers can anonymously share large volumes of illegal material amongst each other from locations with relaxed or non-existent laws that prohibit the possession or trafficking of illegal material. Likewise the internet is home to new developing social networks on the world wide web where young people are attracted to sharing their personal information amongst friends and family and inevitably become targets of predators. The volume and availability of such content, or targets for predators can be an overwhelming task for law enforcement to track and/or catalogue. In general cases image collections can range in the thousands (Taylor and Quayle 2003), and to assist in the identification and classification of child pornography within these large collections, the research of this author’s PhD seeks to establish a automated method of identifying and classifying material that has a high probability of being child pornography. This paper establishes the working progress of the author as they review existing relevant literature and hypothesise possible methods of identification and classification

Automatic detection of child pornography: A research in progress

Before the introduction of the internet, the availability of child pornography was reported as on the decline (Jenkins 2001). Since its emergence, however, the internet has made child pornography a much more accessible and available means of trafficking across borders (Biegel 2001; Jenkins 2001; Wells, Finkelhor et al. 2007). The internet as it is at present is made up of a vast array of protocols and networks where traffickers can anonymously share large volumes of illegal material amongst each other from locations with relaxed or non-existent laws that prohibit the possession or trafficking of illegal material. Likewise the internet is home to new developing social networks on the world wide web where young people are attracted to sharing their personal information amongst friends and family and inevitably become targets of predators. The volume and availability of such content, or targets for predators can be an overwhelming task for law enforcement to track and/or catalogue. In general cases image collections can range in the thousands (Taylor and Quayle 2003), and to assist in the identification and classification of child pornography within these large collections, the research of this author’s PhD seeks to establish a automated method of identifying and classifying material that has a high probability of being child pornography. This paper establishes the working progress of the author as they review existing relevant literature and hypothesise possible methods of identification and classificatio