In this short note we introduce ResearchDoom, an implementation of the Doom first-person shooter that can extract detailed metadata from the game. We also introduce the CocoDoom dataset, a collection of pre-recorded data extracted from Doom gaming sessions along with annotations in the MS Coco format. ResearchDoom and CocoDoom can be used to train and evaluate a variety of computer vision methods such as object recognition, detection and segmentation at the level of instances and categories, tracking, ego-motion estimation, monocular depth estimation and scene segmentation. The code and data are available at this http URL
Another recent effort similar to ResearchDoom is Unreal CV @cite_5 . This engine can also extract metadata similar to ResearchDoom and, by building on the Unreal engine, can potentially be applicable to a huge variety of modern video games. ResearchDoom is much simpler than Unreal CV as it applies to a single and relatively old game. While Doom incorporates several limitations, such as the fact that the camera can only rotate around the vertical axis, the data is nevertheless fairly complex. Furthermore, Doom provides a relatively restricted and consistent world form which data can be extracted for experiments. We leverage the latter fact to define and provide abundant pre-computed data with detailed metadata as well as with annotations using the Microsoft Coco format.
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UnrealCV is a project to help computer vision researchers build virtual worlds using Unreal Engine 4 (UE4). It extends UE4 with a plugin by providing (1) A set of UnrealCV commands to interact with the virtual world. (2) Communication between UE4 and an external program, such as Caffe. UnrealCV can be used in two ways. The first one is using a compiled game binary with UnrealCV embedded. This is as simple as running a game, no knowledge of Unreal Engine is required. The second is installing UnrealCV plugin to Unreal Engine 4 (UE4) and use the editor of UE4 to build a new virtual world. UnrealCV is an open-source software under the MIT license. Since the initial release in September 2016, it has gathered an active community of users, including students and researchers. Computer graphics can not only generate synthetic images and ground truth but it also offers the possibility of constructing virtual worlds in which: (i) an agent can perceive, navigate, and take actions guided by AI algorithms, (ii) properties of the worlds can be modified (e.g., material and reflectance), (iii) physical simulations can be performed, and (iv) algorithms can be learnt and evaluated. But creating realistic virtual worlds is not easy. The game industry, however, has spent a lot of effort creating 3D worlds, which a player can interact with. So researchers can build on these resources to create virtual worlds, provided we can access and modify the internal data structures of the games. To enable this we created an open-source plugin UnrealCV (this http URL) for a popular game engine Unreal Engine 4 (UE4). We show two applications: (i) a proof of concept image dataset, and (ii) linking Caffe with the virtual world to test deep network algorithms. Computer graphics can not only generate synthetic images and ground truth but it also offers the possibility of constructing virtual worlds in which: (i) an agent can perceive, navigate, and take actions guided by AI algorithms, (ii) properties of the worlds can be modified (e.g., material and reflectance), (iii) physical simulations can be performed, and (iv) algorithms can be learnt and evaluated. But creating realistic virtual worlds is not easy. The game industry, however, has spent a lot of effort creating 3D worlds, which a player can interact with. So researchers can build on these resources to create virtual worlds, provided we can access and modify the internal data structures of the games. To enable this we created an open-source plugin UnrealCV (Project website: http: unrealcv.github.io) for a popular game engine Unreal Engine 4 (UE4). We show two applications: (i) a proof of concept image dataset, and (ii) linking Caffe with the virtual world to test deep network algorithms. Data-driven algorithms have surpassed traditional techniques in almost every aspect in robotic vision problems. Such algorithms need vast amounts of quality data to be able to work properly after their training process. Gathering and annotating that sheer amount of data in the real world is a time-consuming and error-prone task. Those problems limit scale and quality. Synthetic data generation has become increasingly popular since it is faster to generate and automatic to annotate. However, most of the current datasets and environments lack realism, interactions, and details from the real world. UnrealROX is an environment built over Unreal Engine 4 which aims to reduce that reality gap by leveraging hyperrealistic indoor scenes that are explored by robot agents which also interact with objects in a visually realistic manner in that simulated world. Photorealistic scenes and robots are rendered by Unreal Engine into a virtual reality headset which captures gaze so that a human operator can move the robot and use controllers for the robotic hands; scene information is dumped on a per-frame basis so that it can be reproduced offline to generate raw data and ground truth annotations. This virtual reality environment enables robotic vision researchers to generate realistic and visually plausible data with full ground truth for a wide variety of problems such as class and instance semantic segmentation, object detection, depth estimation, visual grasping, and navigation. Video games are a compelling source of annotated data as they can readily provide fine-grained groundtruth for diverse tasks. However, it is not clear whether the synthetically generated data has enough resemblance to the real-world images to improve the performance of computer vision models in practice. We present experiments assessing the effectiveness on real-world data of systems trained on synthetic RGB images that are extracted from a video game. We collected over 60000 synthetic samples from a modern video game with similar conditions to the real-world CamVid and Cityscapes datasets. We provide several experiments to demonstrate that the synthetically generated RGB images can be used to improve the performance of deep neural networks on both image segmentation and depth estimation. These results show that a convolutional network trained on synthetic data achieves a similar test error to a network that is trained on real-world data for dense image classification. Furthermore, the synthetically generated RGB images can provide similar or better results compared to the real-world datasets if a simple domain adaptation technique is applied. Our results suggest that collaboration with game developers for an accessible interface to gather data is potentially a fruitful direction for future work in computer vision. A number of recent approaches to policy learning in 2D game domains have been successful going directly from raw input images to actions. However when employed in complex 3D environments, they typically suffer from challenges related to partial observability, combinatorial exploration spaces, path planning, and a scarcity of rewarding scenarios. Inspired from prior work in human cognition that indicates how humans employ a variety of semantic concepts and abstractions (object categories, localisation, etc.) to reason about the world, we build an agent-model that incorporates such abstractions into its policy-learning framework. We augment the raw image input to a Deep Q-Learning Network (DQN), by adding details of objects and structural elements encountered, along with the agent's localisation. The different components are automatically extracted and composed into a topological representation using on-the-fly object detection and 3D-scene reconstruction.We evaluate the efficacy of our approach in Doom, a 3D first-person combat game that exhibits a number of challenges discussed, and show that our augmented framework consistently learns better, more effective policies. In training deep neural networks for semantic segmentation, the main limiting factor is the low amount of ground truth annotation data that is available in currently existing datasets. The limited availability of such data is due to the time cost and human effort required to accurately and consistently label real images on a pixel level. Modern sandbox video game engines provide open world environments where traffic and pedestrians behave in a pseudo-realistic manner. This caters well to the collection of a believable road-scene dataset. Utilizing open-source tools and resources found in single-player modding communities, we provide a method for persistent, ground truth, asset annotation of a game world. By collecting a synthetic dataset containing upwards of @math images, we demonstrate real-time, on-demand, ground truth data annotation capability of our method. Supplementing this synthetic data to Cityscapes dataset, we show that our data generation method provides qualitative as well as quantitative improvements---for training networks---over previous methods that use video games as surrogate. Psychlab is a simulated psychology laboratory inside the first-person 3D game world of DeepMind Lab ( 2016). Psychlab enables implementations of classical laboratory psychological experiments so that they work with both human and artificial agents. Psychlab has a simple and flexible API that enables users to easily create their own tasks. As examples, we are releasing Psychlab implementations of several classical experimental paradigms including visual search, change detection, random dot motion discrimination, and multiple object tracking. We also contribute a study of the visual psychophysics of a specific state-of-the-art deep reinforcement learning agent: UNREAL ( 2016). This study leads to the surprising conclusion that UNREAL learns more quickly about larger target stimuli than it does about smaller stimuli. In turn, this insight motivates a specific improvement in the form of a simple model of foveal vision that turns out to significantly boost UNREAL's performance, both on Psychlab tasks, and on standard DeepMind Lab tasks. By open-sourcing Psychlab we hope to facilitate a range of future such studies that simultaneously advance deep reinforcement learning and improve its links with cognitive science. Developing and testing algorithms for autonomous vehicles in real world is an expensive and time consuming process. Also, in order to utilize recent advances in machine intelligence and deep learning we need to collect a large amount of annotated training data in a variety of conditions and environments. We present a new simulator built on Unreal Engine that offers physically and visually realistic simulations for both of these goals. Our simulator includes a physics engine that can operate at a high frequency for real-time hardware-in-the-loop (HITL) simulations with support for popular protocols (e.g. MavLink). The simulator is designed from the ground up to be extensible to accommodate new types of vehicles, hardware platforms and software protocols. In addition, the modular design enables various components to be easily usable independently in other projects. We demonstrate the simulator by first implementing a quadrotor as an autonomous vehicle and then experimentally comparing the software components with real-world flights. In order to experiment with machine learning and data mining techniques in the domain of Real-Time Strategy games such as StarCraft, a dataset is required that captures the complex detail of the interactions taking place between the players and the game. This paper describes a new extraction process by which game data is extracted both directly from game log (replay) files, and indirectly through simulating the replays within the StarCraft game engine. Data is then stored in a compact, hierarchical, and easily accessible format. This process is applied to a collection of expert replays, creating a new standardised dataset. The data recorded is enough for almost the complete game state to be reconstructed, from either player's viewpoint, at any point in time (to the nearest second). This process has revealed issues in some of the source replay files, as well as discrepancies in prior datasets. Where practical, these errors have been removed in order to produce a higher-quality reusable dataset. A visible area detection method that depends on online available information is proposed.Visual popularity and sentiment are calculated based on images and their accompanying text that people share in social mediaThe visual popularity information is presented in a 3D virtual environment using Unreal engine. Display Omitted This paper proposes to use a geotagged virtual world for the visualization of peoples visual interest and their sentiment as captured from their social network activities. Using mobile devices, people widely share their experiences and the things they find interesting through social networks. We experimentally show that accumulating information over a period of time from multiple social network users allows to efficiently map and visualize popular landmarks as found in cities such as Rome in Italy and Dublin in Ireland. The proposed approach is also sensitive to temporal and spatial events that attract visual attention. We visualize the calculated popularity on 3D virtual cities using game engine technologies. We present a photo-realistic training and evaluation simulator (Sim4CV) (http: www.sim4cv.org) with extensive applications across various fields of computer vision. Built on top of the Unreal Engine, the simulator integrates full featured physics based cars, unmanned aerial vehicles (UAVs), and animated human actors in diverse urban and suburban 3D environments. We demonstrate the versatility of the simulator with two case studies: autonomous UAV-based tracking of moving objects and autonomous driving using supervised learning. The simulator fully integrates both several state-of-the-art tracking algorithms with a benchmark evaluation tool and a deep neural network architecture for training vehicles to drive autonomously. It generates synthetic photo-realistic datasets with automatic ground truth annotations to easily extend existing real-world datasets and provides extensive synthetic data variety through its ability to reconfigure synthetic worlds on the fly using an automatic world generation tool. Now a days, the Data Engineering becoming emerging trend to discover knowledge from web audio- visual data such as- YouTube videos, Yahoo Screen, Face Book videos etc. Different categories of web video are being shared on such social websites and are being used by the billions of users all over the world. The uploaded web videos will have different kind of metadata as attribute information of the video data. The metadata attributes defines the contents and features characteristics of the web videos conceptually. Hence, accomplishing web video mining by extracting features of web videos in terms of metadata is a challenging task. In this work, effective attempts are made to classify and predict the metadata features of web videos such as length of the web videos, number of comments of the web videos, ratings information and view counts of the web videos using data mining algorithms such as Decision tree J48 and navie Bayesian algorithms as a part of web video mining. The results of Decision tree J48 and navie Bayesian classification models are analyzed and compared as a step in the process of knowledge discovery from web videos. Representing the semantics of unstructured scientific publications will certainly facilitate access and search and hopefully lead to new discoveries. However, current digital libraries are usually limited to classic flat structured metadata even for scientific publications that potentially contain rich semantic metadata. In addition, how to search the scientific literature of linked semantic metadata is an open problem. We have developed a semantic digital library oreChem ChemxSeer that models chemistry papers with semantic metadata. It stores and indexes extracted metadata from a chemistry paper repository Chemx Seer using "compound objects". We use the Open Archives Initiative Object Reuse and Exchange (OAI-ORE) (http: www.openarchives.org ore standard to define a compound object that aggregates metadata fields related to a digital object. Aggregated metadata can be managed and retrieved easily as one unit resulting in improved ease-of-use and has the potential to improve the semantic interpretation of shared data. We show how metadata can be extracted from documents and aggregated using OAI-ORE. ORE objects are created on demand; thus, we are able to search for a set of linked metadata with one query. We were also able to model new types of metadata easily. For example, chemists are especially interested in finding information related to experiments in documents. We show how paragraphs containing experiment information in chemistry papers can be extracted and tagged based on a chemistry ontology with 470 classes, and then represented in ORE along with other document-related metadata. Our algorithm uses a classifier with features that are words that are typically only used to describe experiments, such as "apparatus", "prepare", etc. Using a dataset comprised of documents from the Royal Society of Chemistry digital library, we show that the our proposed methodperforms well in extracting experiment-related paragraphs from chemistry documents. The design and implementation of a search engine for lecture webcasts is described. A searchable text index is created allowing users to locate material within lecture videos found on a variety of websites such as YouTube and Berkeley webcasts. The index is created from words on the presentation slides appearing in the video along with any associated metadata such as the title and abstract when available. The video is analyzed to identify a set of distinct slide images, to which OCR and lexical processes are applied which in turn generate a list of indexable terms. Several problems were discovered when trying to identify distinct slides in the video stream. For example, picture-in-picture compositing of a speaker and a presentation slide, switching cameras, and slide builds confuse basic frame-differencing algorithms for extracting keyframe slide images. Algorithms are described that improve slide identification. A prototype system was built to test the algorithms and the utility of the search engine. Users can browse lists of lectures, slides in a specific lecture, or play the lecture video. Over 10,000 lecture videos have been indexed from a variety of sources. A public website will be published in mid 2010 that allows users to experiment with the search engine. In this paper, we describe a method for implementing the behaviour of artificial actors in the context of interactive storytelling. We have developed a fully implemented prototype based on the Unreal Tournament™ game engine, and carried experiments with a simple sitcom-like scenario. We discuss the central role of artificial actors in interactive storytelling and how real-time generation of their behaviour participates in the creation of a dynamic storyline. We follow previous work describing the behaviour of artificial actors through AI planning formalisms, and adapt it to the context of narrative representation. In this context, the narrative equivalent of a character’s behaviour consists in its role. The set of possible roles for a given actor is represented as a Hierarchical Task Network (HTN). The system uses HTN planning to dynamically generate the character roles, by interleaving planning and execution, which supports dynamic interaction between actors, as well as user intervention in the unfolding plot. Finally, we present several examples of short plots and situations generated by the system from the dynamic interaction of artificial actors. Metadata for the World Wide Web is important, but metadata for Peer-to-Peer (P2P) networks is absolutely crucial. In this paper we discuss the open source project Edutella which builds upon metadata standards defined for the WWW and aims to provide an RDF-based metadata infrastructure for P2P applications, building on the recently announced JXTA Framework. We describe the goals and main services this infrastructure will provide and the architecture to connect Edutella Peers based on exchange of RDF metadata. As the query service is one of the core services of Edutella, upon which other services are built, we specify in detail the Edutella Common Data Model (ECDM) as basis for the Edutella query exchange language (RDF-QEL-i) and format implementing distributed queries over the Edutella network. Finally, we shortly discuss registration and mediation services, and introduce the prototype and application scenario for our current Edutella aware peers. This study examines the usability challenges faced by new players of massively multiplayer online role-playing games (MMORPGs), one of the fastest-growing segments of the video game industry. Played in completely online worlds, these games allow players to communicate with one another, form groups and communities, and compete in a variety of fantasy environments.Nineteen subjects participated in an exploratory usability study of four games, three MMORPGs and a similar single-player game used for comparison. Results reveal that many people not usually considered as potential players of these games may be interested in them, but a wide variety of usability issues present serious problems for players inexperienced with the genre. Based on an analysis of the usability data and player feedback, specific recommendations are made to improve the experience of these games for new players. These results further demonstrate the applicability and importance of usability testing to video games. In this paper we explore the use of Hierarchical-Task-Network (HTN) representations to model strategic game AI. We will present two case studies. The first one reports on an experiment using HTNs to model strategies for Unreal Tournament® (UT) bots. We will argue that it is possible to encode strategies that coordinate teams of bots in first-person shooter games using HTNs. The second one compares an alternative to HTNs called Task-Method-Knowledge (TMK) process models. TMK models are of interest to game AI because, as we will show, they are as expressive as HTNs but have more convenient syntax. Therefore, HTN planners can be used to generate correct plans for coordinated team AI behavior modeled with TMK representations. We present a novel application of the Kinect™, an input device designed for the Microsoft® Xbox 360® video game system. The device can be used by Earth scientists as a low-cost, high-resolution, short-range 3D 4D camera imaging system producing data similar to a terrestrial light detection and ranging (LiDAR) sensor. The Kinect contains a structured light emitter, an infrared camera (the combination of these two produce a distance image), a visual wavelength camera, a three-axis accelerometer, and four microphones. The cost is US $100, frame rate is 30 Hz, spatial and depth resolutions are mm to cm depending on range, and the optimal operating range is 0.5 to 5 m. The resolution of the distance measurements decreases with distance and is ≤1 mm at 0.5 m and 75 mm at 5 m. We illustrate data collection and basic data analysis routines in three experiments designed to demonstrate the breadth and utility of this new sensor in domains of glaciology, stream bathymetry, and geomorphology, although the device is applicable to a number of other Earth science fields. Copyright © 2012 John Wiley & Sons, Ltd.