Multi-camera analysis of soccer sequences

The automatic detection of meaningful phases in a soccer game depends on the accurate localization of players and the ball at each moment. However, the automatic analysis of soccer sequences is a challenging task due to the presence of fast moving multiple objects. For this purpose, we present a multi-camera analysis system that yields the position of the ball and players on a common ground plane. The detection in each camera is based on a code-book algorithm and different features are used to classify the detected blobs. The detection results of each camera are transformed using homography to a virtual top-view of the playing field. Within this virtual top-view we merge trajectory information of the different cameras allowing to refine the found positions. In this paper, we evaluate the system on a public SOCCER dataset and end with a discussion of possible improvements of the dataset

Large-Scale Mapping of Human Activity using Geo-Tagged Videos

This paper is the first work to perform spatio-temporal mapping of human activity using the visual content of geo-tagged videos. We utilize a recent deep-learning based video analysis framework, termed hidden two-stream networks, to recognize a range of activities in YouTube videos. This framework is efficient and can run in real time or faster which is important for recognizing events as they occur in streaming video or for reducing latency in analyzing already captured video. This is, in turn, important for using video in smart-city applications. We perform a series of experiments to show our approach is able to accurately map activities both spatially and temporally. We also demonstrate the advantages of using the visual content over the tags/titles.Comment: Accepted at ACM SIGSPATIAL 201

Soccer on Your Tabletop

We present a system that transforms a monocular video of a soccer game into a moving 3D reconstruction, in which the players and field can be rendered interactively with a 3D viewer or through an Augmented Reality device. At the heart of our paper is an approach to estimate the depth map of each player, using a CNN that is trained on 3D player data extracted from soccer video games. We compare with state of the art body pose and depth estimation techniques, and show results on both synthetic ground truth benchmarks, and real YouTube soccer footage.Comment: CVPR'18. Project: http://grail.cs.washington.edu/projects/soccer

Capturing the sporting heroes of our past by extracting 3D movements from legacy video content

Sports are a key part of cultural identity, and it is necessary to preserve them as important intangible Cultural Heritage, especially the human motion techniques specific to individual sports. In this paper we present a method for extracting 3D athlete motion from video broadcast sources, providing an important tool for preserving the heritage represented by these movements. Broadcast videos include camera motion, multiple player interaction, occlusions and noise, presenting significant challenges to solve the reconstruction. The approach requires initial definition of some key-frames and setting of 2D key-points in those frames manually. Thereafter an automatic process estimates the poses and positions of the players in the key-frames, and in the frames between key-frames, taking into account collisions with the environment and human kinematic constraints. Initial results are extremely promising and this data could be used to analyze the sport's evolution over time, or even to generate animations for interactive applications

Action Recognition in Videos: from Motion Capture Labs to the Web

This paper presents a survey of human action recognition approaches based on visual data recorded from a single video camera. We propose an organizing framework which puts in evidence the evolution of the area, with techniques moving from heavily constrained motion capture scenarios towards more challenging, realistic, "in the wild" videos. The proposed organization is based on the representation used as input for the recognition task, emphasizing the hypothesis assumed and thus, the constraints imposed on the type of video that each technique is able to address. Expliciting the hypothesis and constraints makes the framework particularly useful to select a method, given an application. Another advantage of the proposed organization is that it allows categorizing newest approaches seamlessly with traditional ones, while providing an insightful perspective of the evolution of the action recognition task up to now. That perspective is the basis for the discussion in the end of the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4 table