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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50863

Title: Model-based approach to spatial–temporal sampling of video clips for video object detection by classification
Authors: Chi-Han Chuang
Shyi-Chyi Cheng
Chin-Chun Chang
Yi-Ping Phoebe Chen
Contributors: 國立臺灣海洋大學:資訊工程學系
Keywords: Semantic video objects
Spatial–temporal sampling
Human action detection
Video object model
Dynamic programming
Multiple alignment
Model-based tracking
Video object detetcion
Date: 2014-05
Issue Date: 2018-10-29T02:37:45Z
Publisher: Journal of Visual Communication and Image Representation
Abstract: Abstract: For a variety of applications such as video surveillance and event annotation, the spatial–temporal boundaries between video objects are required for annotating visual content with high-level semantics. In this paper, we define spatial–temporal sampling as a unified process of extracting video objects and computing their spatial–temporal boundaries using a learnt video object model. We first provide a computational approach for learning an optimal key-object codebook sequence from a set of training video clips to characterize the semantics of the detected video objects. Then, dynamic programming with the learnt codebook sequence is used to locate the video objects with spatial–temporal boundaries in a test video clip. To verify the performance of the proposed method, a human action detection and recognition system is constructed. Experimental results show that the proposed method gives good performance on several publicly available datasets in terms of detection accuracy and recognition rate.
Relation: 25(5) pp.1018-1030
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50863
Appears in Collections:[資訊工程學系] 期刊論文

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