Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/134421
Title: FEATURE MINING IN TEMPORAL HUMAN ACTION LOCALIZATION
Authors: YUAN JUN
Keywords: Action, Temporal Localization
Issue Date: 18-Aug-2016
Source: YUAN JUN (2016-08-18). FEATURE MINING IN TEMPORAL HUMAN ACTION LOCALIZATION. ScholarBank@NUS Repository.
Abstract: Temporal human action localization aims at detecting and labeling actions in untrimmed video sequences, which is a generalization of the traditional action classification problem and better models the real-life video content analysis. However, the localization problem is difficult due to the uncertainties in action occurrence and is still being developed at a preliminary stage. This research proposes the Pyramid of Score Distribution Features (PSDF) to address the uncertainties of action position, duration and label in untrimmed videos. The PSDF carries heuristic information of action occurrence, and can be calculated via an efficient recursive addition. The temporal contexts are further modeled by two recurrent structures, the Elman-Net and LSTM to further enhance the performance of action localization.
URI: http://scholarbank.nus.edu.sg/handle/10635/134421
Appears in Collections:Ph.D Theses (Restricted)

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