Activity-conditioned continuous human pose estimation for performance analysis of athletes using the example of swimming

In this paper we consider the problem of human pose estimation in real-world videos of swimmers. Swimming channels allow filming swimmers simultaneously above and below the water surface with a single stationary camera. These recordings can be used to quantitatively assess the athletes' performance. The quantitative evaluation, so far, requires manual annotations of body parts in each video frame. We therefore apply the concept of CNNs in order to automatically infer the required pose information. Starting with an off-the-shelf architecture, we develop extensions to leverage activity information - in our case the swimming style of an athlete - and the continuous nature of the video recordings. Our main contributions are threefold: (a) We apply and evaluate a fine-tuned Convolutional Pose Machine architecture as a baseline in our very challenging aquatic environment and discuss its error modes, (b) we propose an extension to input swimming style information into the fully convolutional architecture and (c) modify the architecture for continuous pose estimation in videos. With these additions we achieve reliable pose estimates with up to +16% more correct body joint detections compared to the baseline architecture.Comment: 10 pages, 9 figures, accepted at WACV 201

De/construction sites: Romans and the digital playground

The Roman world as attested to archaeologically and as interacted with today has its expression in a great many computational and other media. The place of visualisation within this has been paramount. This paper argues that the process of digitally constructing the Roman world and the exploration of the resultant models are useful methods for interpretation and influential factors in the creation of a popular Roman aesthetic. Furthermore, it suggests ways in which novel computational techniques enable the systematic deconstruction of such models, in turn re-purposing the many extant representations of Roman architecture and material culture

Portmerion, Proportion and Perspective

The holiday village of Portmerion was created by Bertram Clough Williams-Ellis (1883 1978) over a period of fifty-one years, starting in 1926. It was grade II listed in 1971. However, Portmerion has become a part of western popular culture rather than of mainstream architectural history. Its use as the setting for the cult 1967 television series “The Prisoner” ensures continued worldwide interest and a constant stream of visitors. Williams Ellis’ design methods were empirical, initial designs being adjusted by eye on site in close collaboration with trusted builders. This paper analyses the development of Portmerion as a gesamtkunstwerk; considering the experience of movement through the village as a dynamic composition of shifting vistas, focussing the visitor on a series of constructed views. Through this analysis, Portmerion is revealed as both a manifestation of the architecture of pleasure and an exercise in the pleasure of architecture

CAR-Net: Clairvoyant Attentive Recurrent Network

We present an interpretable framework for path prediction that leverages dependencies between agents' behaviors and their spatial navigation environment. We exploit two sources of information: the past motion trajectory of the agent of interest and a wide top-view image of the navigation scene. We propose a Clairvoyant Attentive Recurrent Network (CAR-Net) that learns where to look in a large image of the scene when solving the path prediction task. Our method can attend to any area, or combination of areas, within the raw image (e.g., road intersections) when predicting the trajectory of the agent. This allows us to visualize fine-grained semantic elements of navigation scenes that influence the prediction of trajectories. To study the impact of space on agents' trajectories, we build a new dataset made of top-view images of hundreds of scenes (Formula One racing tracks) where agents' behaviors are heavily influenced by known areas in the images (e.g., upcoming turns). CAR-Net successfully attends to these salient regions. Additionally, CAR-Net reaches state-of-the-art accuracy on the standard trajectory forecasting benchmark, Stanford Drone Dataset (SDD). Finally, we show CAR-Net's ability to generalize to unseen scenes.Comment: The 2nd and 3rd authors contributed equall

History navigation in location-based mobile systems

The aim of this paper is to provide an overview and comparison of concepts that have been proposed to guide users through interaction histories (e.g. for web browsers). The goal is to gain insights into history design that may be used for designing an interaction history for the location-based Tourist Information Provider (TIP) system [8]. The TIP system consists of several services that interact on a mobile device