HCMC '15 Pub Date : 2015-10-30 DOI: 10.1145/2810397.2810399

Muhammad Sikandar Lal Khan, Haibo Li, S. Réhman

引用次数: 2

Event Detection and Highlight Detection of Broadcasted Game Videos 游戏视频广播的事件检测与高亮检测

HCMC '15 Pub Date : 2015-10-30 DOI: 10.1145/2810397.2810398

W. Chu, Yung-Chieh Chou

引用次数: 9

Multimedia Fatigue Detection for Adaptive Infotainment User Interface 自适应信息娱乐用户界面多媒体疲劳检测

HCMC '15 Pub Date : 2015-10-30 DOI: 10.1145/2810397.2810400

Sultan Alhazmi, M. Saini, Abdulmotaleb El Saddik

{"title":"Multimedia Fatigue Detection for Adaptive Infotainment User Interface","authors":"Sultan Alhazmi, M. Saini, Abdulmotaleb El Saddik","doi":"10.1145/2810397.2810400","DOIUrl":"https://doi.org/10.1145/2810397.2810400","url":null,"abstract":"Current vehicles are equipped with user interfaces that assist the drivers by presenting essential information such as navigation, speed limit, etc. In this work we present a fatigue detection model in order to build an adaptive user interface for vehicles that changes its properties according to the fatigue level of the driver. When the driver is fatigued, the interface parameters, such as intensity and color combination, are modulated to make the user interface more attentive and intrusive. At other times, when the driver is not fatigued, the interface properties are optimized for aesthetics and pleasure. In this way, the adaptive interface provides a warning to the driver while she is fatigued along with the routine essential information. We take a multimedia approach to measure fatigue by analysing the driver behaviour. The system captures driver behaviour with four media streams that capture: angular velocity of steering wheel, force on brake pedal, force on gas pedal, and grip force. These continuous media stream are fused together with other contextual parameters to detect fatigue. In the experiments, we evaluate two fusion techniques and 16 media stream combinations. It is found that the fatigue detection accuracy increases almost linearly with number of media streams fused. We also found that the steering wheel provides best cue of fatigue, while the gas pedal provides weakest cue. Personalized Bayesian Networks further enhance the accuracy.","PeriodicalId":253945,"journal":{"name":"HCMC '15","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127044043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Highly Accurate and Fully Automatic Head Pose Estimation from a Low Quality Consumer-Level RGB-D Sensor 基于低质量消费级RGB-D传感器的高精度全自动头部姿态估计

HCMC '15 Pub Date : 2015-10-30 DOI: 10.1145/2810397.2810401

R. S. Ghiass, Ognjen Arandjelovic, D. Laurendeau

{"title":"Highly Accurate and Fully Automatic Head Pose Estimation from a Low Quality Consumer-Level RGB-D Sensor","authors":"R. S. Ghiass, Ognjen Arandjelovic, D. Laurendeau","doi":"10.1145/2810397.2810401","DOIUrl":"https://doi.org/10.1145/2810397.2810401","url":null,"abstract":"In this paper we describe a novel algorithm for head pose estimation from low-quality RGB-D data acquired using a consumer-level device such as Microsoft Kinect. We focus our attention on the well-known challenges in the processing of depth point-clouds which include spurious data, noise, and missing data caused by occlusion. Our algorithm performs pose estimation by fitting a 3D morphable model which explicitly includes pose parameters. Several important novelties are described. (i) We propose a method for automatic removal of the majority of spurious depth data which uses facial feature detection in the associated RGB image. By back-projecting the corresponding image loci and intersecting them with the 3D point-cloud we construct the facial features plane used to crop the point-cloud. (ii) Both high convergence speed and high fitting accuracy are achieved by formulating the fitting objective function to include both point-to-point and point-to-plane point-cloud matching terms. (iii) The effect of misleading point-cloud matches caused by noisy or missing data is reduced by using the Tukey biweight function as a robust statistic and by employing a re-weighting scheme for different terms in the fitting objective function. (iv) Lastly, the proposed algorithm is evaluated on the standard benchmark Biwi Kinect Head Pose Database on which it is shown to outperform substantially the current state-of-the-art, achieving more than a 20-fold reduction in error estimates of all three Euler angles i.e. yaw, pitch, and roll. A thorough analysis of the results is used both to gain full insight into the behaviour of the described algorithm as well as to highlight important methodological issues which future authors should consider in the evaluation of pose estimation algorithms.","PeriodicalId":253945,"journal":{"name":"HCMC '15","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115447542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 28

HCMC '15最新文献