Knowledge Resource Center for Ecological Environment in Arid Area
| Video Analytics with Spatio-Temporal Characteristics of Activities | |
| Cheng;Guangchun | |
| 出版年 | 2015 |
| 学位授予单位 | University of North Texas |
| 英文摘要 | As video capturing devices become more ubiquitous from surveillance cameras to smart phones, the demand of automated video analysis is increasing as never before. One obstacle in this process is to efficiently locate where a human operator’s attention should be, and another is to determine the specific types of activities or actions without ambiguity. It is the special interest of this dissertation to locate spatial and temporal regions of interest in videos and to develop a better action representation for video-based activity analysis. This dissertation follows the scheme of “locating then recognizing” activities of interest in videos, i.e., locations of potentially interesting activities are estimated before performing in-depth analysis. Theoretical properties of regions of interest in videos are first exploited, based on which a unifying framework is proposed to locate both spatial and temporal regions of interest with the same settings of parameters. The approach estimates the distribution of motion based on 3D structure tensors, and locates regions of interest according to persistent occurrences of low probability. Two contributions are further made to better represent the actions. The first is to construct a unifying model of spatio-temporal relationships between reusable mid-level actions which bridge low-level pixels and high-level activities. Dense trajectories are clustered to construct mid-level actionlets, and the temporal relationships between actionlets are modeled as Action Graphs based on Allen interval predicates. The second is an effort for a novel and efficient representation of action graphs based on a sparse coding framework. Action graphs are first represented using Laplacian matrices and then decomposed as a linear combination of primitive dictionary items following sparse coding scheme. The optimization is eventually formulated and solved as a determinant maximization problem, and 1-nearest neighbor is used for action classification. The experiments have shown better results than existing approaches for regions-of-interest detection and action recognition. |
| 英文关键词 | Video analytics Action recognition Spatio-temporal Anomaly detection Region of interest Image analysis. Image processing. Pattern recognition systems. |
| 语种 | 英语 |
| URL | https://digital.library.unt.edu/ark:/67531/metadc799541 |
| 来源机构 | University of North Texas |
| 资源类型 | 学位论文 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/249110 |
| 推荐引用方式 GB/T 7714 | Cheng;Guangchun. Video Analytics with Spatio-Temporal Characteristics of Activities[D]. University of North Texas,2015. |
| 条目包含的文件 | 条目无相关文件。 | |||||
| 个性服务 |
| 推荐该条目 |
| 保存到收藏夹 |
| 导出为Endnote文件 |
| 谷歌学术 |
| 谷歌学术中相似的文章 |
| [Cheng;Guangchun]的文章 |
| 百度学术 |
| 百度学术中相似的文章 |
| [Cheng;Guangchun]的文章 |
| 必应学术 |
| 必应学术中相似的文章 |
| [Cheng;Guangchun]的文章 |
| 相关权益政策 |
| 暂无数据 |
| 收藏/分享 |
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
