Proceedings IEEE Workshop on Detection and Recognition of Events in Video最新文献

Segmentation and recognition of continuous human activity 连续人类活动的分割和识别

Proceedings IEEE Workshop on Detection and Recognition of Events in Video Pub Date : 2001-07-08 DOI: 10.1109/EVENT.2001.938863

Anjum Ali, J. Aggarwal

{"title":"Segmentation and recognition of continuous human activity","authors":"Anjum Ali, J. Aggarwal","doi":"10.1109/EVENT.2001.938863","DOIUrl":"https://doi.org/10.1109/EVENT.2001.938863","url":null,"abstract":"This paper presents a methodology for automatic segmentation and recognition of continuous human activity. We segment a continuous human activity into separate actions and correctly identify each action. The camera views the subject from the lateral view: there are no distinct breaks or pauses between the execution of different actions. We have no prior knowledge about the commencement or termination of each action. We compute the angles subtended by three major components of the body with the vertical axis, namely the torso, the upper component of the leg and the lower component of the leg. Using these three angles as a feature vector we classify frames into breakpoint and non-breakpoint frames. Breakpoints indicate an action's commencement or termination. We use single action sequences for the training data set. The test sequences, on the other hand are continuous sequences of human activity that consist of three or more actions in succession. The system has been tested on continuous activity sequences containing actions such as walking, sitting down, standing up, bending, getting up, squatting and rising. It detects the breakpoints and classifies the actions between them.","PeriodicalId":375539,"journal":{"name":"Proceedings IEEE Workshop on Detection and Recognition of Events in Video","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115748044","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: 204

Recognizing action events from multiple viewpoints 从多个视点识别动作事件

Proceedings IEEE Workshop on Detection and Recognition of Events in Video Pub Date : 2001-07-08 DOI: 10.1109/EVENT.2001.938868

T. Syeda-Mahmood, M. Alex O. Vasilescu, Saratendu Sethi

引用次数: 115

Temporal events in all dimensions and scales 所有维度和尺度的时间事件

Proceedings IEEE Workshop on Detection and Recognition of Events in Video Pub Date : 2001-07-08 DOI: 10.1109/EVENT.2001.938870

M. Slaney, D. Ponceleón, James Kaufman

引用次数: 2

Towards a unified framework for tracking and analysis of human motion 朝着一个统一的框架来跟踪和分析人体运动

Proceedings IEEE Workshop on Detection and Recognition of Events in Video Pub Date : 2001-07-08 DOI: 10.1109/EVENT.2001.938865

N. Krahnstoever, M. Yeasin, Rajeev Sharma

引用次数: 34

Hierarchical unsupervised learning of facial expression categories 面部表情分类的分层无监督学习

Proceedings IEEE Workshop on Detection and Recognition of Events in Video Pub Date : 2001-07-08 DOI: 10.1109/EVENT.2001.938872

J. Hoey

引用次数: 54

Detecting independently moving objects and their interactions in georeferenced airborne video 在地理参考机载视频中检测独立运动物体及其相互作用

Proceedings IEEE Workshop on Detection and Recognition of Events in Video Pub Date : 2001-07-08 DOI: 10.1109/EVENT.2001.938861

J. Burns

引用次数: 6

Hierarchical motion history images for recognizing human motion 用于人体运动识别的分层运动历史图像

Proceedings IEEE Workshop on Detection and Recognition of Events in Video Pub Date : 2001-07-08 DOI: 10.1109/EVENT.2001.938864

James W. Davis

引用次数: 206

Content-based video retrieval by integrating spatio-temporal and stochastic recognition of events 结合时空和随机事件识别的基于内容的视频检索

Proceedings IEEE Workshop on Detection and Recognition of Events in Video Pub Date : 2001-07-08 DOI: 10.1109/EVENT.2001.938869

M. Petkovic, W. Jonker

引用次数: 89

View-invariant representation and learning of human action 人类行为的视图不变表示和学习

Proceedings IEEE Workshop on Detection and Recognition of Events in Video Pub Date : 2001-07-08 DOI: 10.1109/EVENT.2001.938867

C. Rao, M. Shah

引用次数: 24

Foreground segmentation using adaptive mixture models in color and depth 前景分割使用自适应混合模型在颜色和深度

Proceedings IEEE Workshop on Detection and Recognition of Events in Video Pub Date : 2001-07-08 DOI: 10.1109/EVENT.2001.938860

M. Harville, G. Gordon, J. Woodfill

{"title":"Foreground segmentation using adaptive mixture models in color and depth","authors":"M. Harville, G. Gordon, J. Woodfill","doi":"10.1109/EVENT.2001.938860","DOIUrl":"https://doi.org/10.1109/EVENT.2001.938860","url":null,"abstract":"Segmentation of novel or dynamic objects in a scene, often referred to as \"background subtraction\" or foreground segmentation\", is a critical early in step in most computer vision applications in domains such as surveillance and human-computer interaction. All previously described, real-time methods fail to handle properly one or more common phenomena, such as global illumination changes, shadows, inter-reflections, similarity of foreground color to background and non-static backgrounds (e.g. active video displays or trees waving in the wind). The advent of hardware and software for real-time computation of depth imagery makes better approaches possible. We propose a method for modeling the background that uses per-pixel, time-adaptive, Gaussian mixtures in the combined input space of depth and luminance-invariant color. This combination in itself is novel, but we further improve it by introducing the ideas of (1) modulating the background model learning rate based on scene activity, and (2) making color-based segmentation criteria dependent on depth observations. Our experiments show that the method possesses much greater robustness to problematic phenomena than the prior state-of-the-art, without sacrificing real-time performance, making it well-suited for a wide range of practical applications in video event detection and recognition.","PeriodicalId":375539,"journal":{"name":"Proceedings IEEE Workshop on Detection and Recognition of Events in Video","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131836431","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: 264