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Do points, levels and leaderboards harm intrinsic motivation? An empirical analysis of common gamification elements 13

Do points, levels and leaderboards harm intrinsic motivation...

引用

1st International Conference on Gameful Design, Research, and Applications, Gamification 2013

作者： Mekler, Elisa D. Brühlmann, Florian Opwis, Klaus Tuch, Alexandre N. Center for Cognitive Psychology and Methodology University of Basel Switzerland Dpt. of Computer Science University of Copenhagen Denmark

ISBN: (纸本)9781450328159

It is heavily debated within the gamification community whether specific game elements may actually undermine users' intrinsic motivation. This online experiment examined the effects of three commonly employed game design elements - points, leaderboard, levels - on users' performance, intrinsic motivation, perceived autonomy and competence in an image annotation task. Implementation of these game elements significantly increased performance, but did not affect perceived autonomy, competence or intrinsic motivation. Our findings suggest that points, levels and leaderboards by themselves neither make nor break users' intrinsic motivation in non-game contexts. Instead, it is assumed that they act as progress indicators, guiding and enhancing user performance. While more research on the contextual factors that may potentially mediate the effects of game elements on intrinsic motivation is required, it seems that the implementation of points, levels, and leaderboards is a viable means to promote specific user behavior in non-game contexts. Copyright © 2013 ACM.

关键词： Motivation

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Articulated Particle Filter for hand tracking

Articulated Particle Filter for hand tracking

引用

21st International Conference on Pattern Recognition, ICPR 2012

作者： Ros, German Del Rincón, Jesús Martinez Mateos, Ginés García Computer Vision Center and Computer Science Dpt. UAB Spain Digital Imaging Research Centre Kingston University London United Kingdom Dpt. Informática y Sistemas University of Murcia Spain

ISBN: (纸本)9784990644109

This paper proposes a new version of Particle Filter, called Articulated Particle Filter - ArPF -, which has been specifically designed for an efficient sampling of hierarchical spaces, generated by articulated objects. Our approach decomposes the articulated motion into layers for efficiency purposes, making use of a careful modeling of the diffusion noise along with its propagation through the articulations. This produces an increase of accuracy and prevent for divergences. The algorithm is tested on hand tracking due to its complex hierarchical articulated nature. With this purpose, a new dataset generation tool for quantitative evaluation is also presented in this paper. © 2012 ICPR Org Committee.

关键词： Monte Carlo methods

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Virtual worlds and active learning for human detection 11

Virtual worlds and active learning for human detection

引用

2011 ACM International Conference on Multimodal Interaction, ICMI'11

作者： Vázquez, David López, Antonio M. Ponsa, Daniel Marín, Javier Computer Vision Center Computer Science Dpt. UAB Campus UAB Bellaterra Spain Computer Vision Center Campus UAB Bellaterra Spain

ISBN: (纸本)9781450306416

Image based human detection is of paramount interest due to its potential applications in fields such as advanced driving assistance, surveillance and media analysis. However, even detecting non-occluded standing humans remains a challenge of intensive research. The most promising human detectors rely on classifiers developed in the discriminative paradigm, i.e. trained with labelled samples. However, labelling is a manual intensive step, especially in cases like human detection where it is necessary to provide at least bounding boxes framing the humans for training. To overcome such problem, some authors have proposed the use of a virtual world where the labels of the different objects are obtained automatically. This means that the human models (classifiers) are learnt using the appearance of rendered images, i.e. using realistic computer graphics. Later, these models are used for human detection in images of the real world. The results of this technique are surprisingly good. However, these are not always as good as the classical approach of training and testing with data coming from the same camera, or similar ones. Accordingly, in this paper we address the challenge of using a virtual world for gathering (while playing a videogame) a large amount of automatically labelled samples (virtual humans and background) and then training a classifier that performs equal, in real-world images, than the one obtained by equally training from manually labelled real-world samples. For doing that, we cast the problem as one of domain adaptation. In doing so, we assume that a small amount of manually labelled samples from real-world images is required. To collect these labelled samples we propose a non-standard active learning technique. Therefore, ultimately our human model is learnt by the combination of virtual and real world labelled samples, which has not been done before. © 2011 ACM.

关键词： Artificial intelligence

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Color contribution to part-based person detection in different types of scenarios

引用

Lecture Notes in computer science

作者： Rao, Muhammad Anwer Vázquez, David López, Antonio M. Computer Vision Center Computer Science Dpt. Universitat Autònoma de Barcelona 08193 Barcelona Spain

ISBN: (纸本)9783642236778

Camera-based person detection is of paramount interest due to its potential applications. The task is difficult because the great variety of backgrounds (scenarios, illumination) in which persons are present, as well as their intra-class variability (pose, clothe, occlusion). In fact, the class person is one of the included in the popular PASCAL visual object classes (VOC) challenge. A breakthrough for this challenge, regarding person detection, is due to Felzenszwalb et al These authors proposed a part-based detector that relies on histograms of oriented gradients (HOG) and latent support vector machines (LatSVM) to learn a model of the whole human body and its constitutive parts, as well as their relative position. Since the approach of Felzenszwalb et al appeared new variants have been proposed, usually giving rise to more complex models. In this paper, we focus on an issue that has not attracted sufficient interest up to now. In particular, we refer to the fact that HOG is usually computed from RGB color space, but other possibilities exist and deserve the corresponding investigation. In this paper we challenge RGB space with the opponent color space (OPP), which is inspired in the human vision system. We will compute the HOG on top of OPP, then we train and test the part-based human classifier by Felzenszwalb et al. using PASCAL VOC challenge protocols and person database. Our experiments demonstrate that OPP outperforms RGB. We also investigate possible differences among types of scenarios: indoor, urban and countryside. Interestingly, our experiments suggest that the benefits of OPP with respect to RGB mainly come for indoor and countryside scenarios, those in which the human visual system was designed by evolution. © 2011 Springer-Verlag.

关键词： RGB color model

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Opponent colors for human detection

Opponent colors for human detection

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Lecture Notes in computer science

作者： Muhammad Anwer, Rao Vázquez, David López, Antonio M. Computer Vision Center Computer Science Dpt. Universitat Autònoma de Barcelona 08193 Bellaterra Barcelona Spain

ISBN: (纸本)9783642212567

Human detection is a key component in fields such as advanced driving assistance and video surveillance. However, even detecting non-occluded standing humans remains a challenge of intensive research. Finding good features to build human models for further detection is probably one of the most important issues to face. Currently, shape, texture and motion features have deserve extensive attention in the literature. However, color-based features, which are important in other domains (e.g., image categorization), have received much less attention. In fact, the use of RGB color space has become a kind of choice by default. The focus has been put in developing first and second order features on top of RGB space (e.g., HOG and co-occurrence matrices, resp.). In this paper we evaluate the opponent colors (OPP) space as a biologically inspired alternative for human detection. In particular, by feeding OPP space in the baseline framework of Dalal et al. for human detection (based on RGB, HOG and linear SVM), we will obtain better detection performance than by using RGB space. This is a relevant result since, up to the best of our knowledge, OPP space has not been previously used for human detection. This suggests that in the future it could be worth to compute co-occurrence matrices, self-similarity features, etc., also on top of OPP space, i.e., as we have done with HOG in this paper. © 2011 Springer-Verlag.

关键词： Security systems

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Network and computer performance in malicious environments: The good, the bad and the ugly

Network and computer performance in malicious environments: ...

引用

International Conference on NETwork Games, Control and Optimization, NetGCooP 2011

作者： Ben-Porat, Udi Bremler-Barr, Anat Levy, Hanoch ETH Zurich Switzerland Computer Science Dpt. Interdisciplinary Center Herzliya Israel Balvatnik School of Computer Science Tel-Aviv University Tel-Aviv Israel

ISBN: (纸本)9781467303842

Performance analysis and the design of computer and networking systems have traditionally accounted for the stochastic nature of the problem addressed and been based on stochastic type analysis, mainly expected value ("the good"). In some related disciplines, mainly computer science and algorithmic design, worst-case analysis ("the bad") have been popular. In recent years we have experienced a wave of DDoS and Cyber attacks threatening the welfare of the internet. These are launched by malicious users whose only incentive is to degrade the performance of other, innocent, users. This has triggered a new direction of research aiming at evaluating system performance while accounting for the malicious behavior of the attackers ("the ugly"). The performance metrics in this case differs from both the average-case and the worst-case and can affect system design considerably. The purpose of this work is to expose and discuss this new analysis approach as well as to distinguish it from the traditional approaches. We use a wide array of cases and results derived in the literature to demonstrate how such analysis can be carried out. We further use them to show what kind of metrics can be used to evaluate the effect of malicious behavior and the resilience of the system against them. © 2011 INRIA.

关键词： Stochastic systems

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An asymmetric fingerprinting scheme based on tardos codes

引用

Lecture Notes in computer science

作者： Charpentier, Ana Fontaine, Caroline Furon, Teddy Cox, Ingemar INRIA-Rennes Research Center Campus de Beaulieu Rennes France CNRS/Lab.-STICC/CID Télécom Bretagne/ITI Brest France University College London Dpt. of Computer Science London United Kingdom

ISBN: (纸本)9783642241772

Asymmetric fingerprinting protocols are designed to prevent an untrustworthy Provider incriminating an innocent Buyer. These protocols enable the Buyer to generate their own fingerprint by themself, and ensure that the Provider never has access to the Buyer's copy of the Work. Until recently, such protocols were not practical because the collusion-resistant codes they rely on were too long. However, the advent of Tardos codes means that the probabilistic collusion-resistant codes are now sufficiently short that asymmetric fingerprint codes should, in theory, be practical. Unfortunately, previous asymmetric fingerprinting protocols cannot be directly applied to Tardos codes, because generation of the Tardos codes depends on a secret vector that is only known to the Provider. This knowledge allows an untrustworthy Provider to attack traditional asymmetric fingerprinting protocols. We describe this attack, and then propose a new asymmetric fingerprinting protocol, specifically designed for Tardos codes. © 2011 Springer-Verlag.

关键词： Sales

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Learning Appearance in Virtual Scenarios for Pedestrian Detection

Learning Appearance in Virtual Scenarios for Pedestrian Dete...

引用

IEEE Conference on computer vision and Pattern Recognition

作者： Javier Marin David Vazquez David Geronimo Antonio M. Lopez Computer Vision Center and Computer Science Dpt. UAB

ISBN: (纸本)9781424469840;9781424469857

Detecting pedestrians in images is a key functionality to avoid vehicle-to-pedestrian collisions. The most promising detectors rely on appearance-based pedestrian classifiers trained with labelled samples. This paper addresses the following question: can a pedestrian appearance model learnt in virtual scenarios work successfully for pedestrian detection in real images? (Fig. 1). Our experiments suggest a positive answer, which is a new and relevant conclusion for research in pedestrian detection. More specifically, we record training sequences in virtual scenarios and then appearance-based pedestrian classifiers are learnt using HOG and linear SVM. We test such classifiers in a publicly available dataset provided by Daimler AG for pedestrian detection benchmarking. This dataset contains real world images acquired from a moving car. The obtained result is compared with the one given by a classifier learnt using samples coming from real images. The comparison reveals that, although virtual samples were not specially selected, both virtual and real based training give rise to classifiers of similar performance.

关键词： Pedestrians Classifiers Virtual training sequence images

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3D Scene Priors for Road Detection

3D Scene Priors for Road Detection

引用

IEEE Conference on computer vision and Pattern Recognition

作者： Jose M. Alvarez Theo Gevers Antonio M. Lopez Computer Vision Center and Computer Science Dpt. Univ. Autonoma de Barcelona Faculty of Science University of Amsterdam

ISBN: (纸本)9781424469840;9781424469857

vision-based road detection is important in different areas of computer vision such as autonomous driving, car collision warning and pedestrian crossing detection. However, current vision-based road detection methods are usually based on low-level features and they assume structured roads, road homogeneity, and uniform lighting conditions. Therefore, in this paper, contextual 3D information is used in addition to low-level cues. Low-level photometric invariant cues are derived from the appearance of roads. Contextual cues used include horizon lines, vanishing points, 3D scene layout and 3D road stages. Moreover, temporal road cues are included. All these cues are sensitive to different imaging conditions and hence are considered as weak cues. Therefore, they are combined to improve the overall performance of the algorithm. To this end, the low-level, contextual and temporal cues are combined in a Bayesian framework to classify road sequences. Large scale experiments on road sequences show that the road detection method is robust to varying imaging conditions, road types, and scenarios (tunnels, urban and highway). Further, using the combined cues outperforms all other individual cues. Finally, the proposed method provides highest road detection accuracy when compared to state-of-the-art methods.

关键词： Road images computer vision LOW LEVEL inspection methods large scale experiment

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Geographic information for vision-based road detection

Geographic information for vision-based road detection

引用

IEEE Symposium on Intelligent Vehicle

作者： José M. Alvarez Felipe Lumbreras Theo Gevers Antonio M. López Computer Vision Center and Computer Science Dpt. Univ. Autonoma de Barcelona Spain Intelligent Systems Lab Amsterdam University of Amsterdam and Comp. Vision Center Barcelona Spain

Road detection is a vital task for the development of autonomous vehicles. The knowledge of the free road surface ahead of the target vehicle can be used for autonomous driving, road departure warning, as well as to support advanced driver assistance systems like vehicle or pedestrian detection. Using vision to detect the road has several advantages in front of other sensors: richness of features, easy integration, low cost or low power consumption. Common vision-based road detection approaches use low-level features (such as color or texture) as visual cues to group pixels exhibiting similar properties. However, it is difficult to foresee a perfect clustering algorithm since roads are in outdoor scenarios being imaged from a mobile platform. In this paper, we propose a novel high-level approach to vision-based road detection based on geographical information. The key idea of the algorithm is exploiting geographical information to provide a rough detection of the road. Then, this segmentation is refined at low-level using color information to provide the final result. The results presented show the validity of our approach.

关键词： Vehicle detection Remotely operated vehicles Road vehicles Mobile robots Vehicle driving Clustering algorithms Sensor phenomena and characterization Costs Energy consumption Image segmentation

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