Temporal Action Detection Based on Hierarchical Object Detection Networks

碩士 === 國立交通大學 === 資訊科學與工程研究所 === 106 === As the development of deep learning, there is a great progress in temporal action detection. Instead of using the ways of conventional computer vision, many approaches use the ways of deep learning to do temporal action detection. There are many applications...

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Bibliographic Details
Main Authors: Wu, Yi-Hui, 巫怡慧
Other Authors: Tsai, Wen-Jiin
Format: Others
Language:en_US
Published: 2018
Online Access:http://ndltd.ncl.edu.tw/handle/g9hpph
Description
Summary:碩士 === 國立交通大學 === 資訊科學與工程研究所 === 106 === As the development of deep learning, there is a great progress in temporal action detection. Instead of using the ways of conventional computer vision, many approaches use the ways of deep learning to do temporal action detection. There are many applications of temporal action detection such as video surveillance and video retrieval. Considering that some actions can be recognized by the information of objects appearing and moving in the video, in this thesis, a hierarchical model is proposed which consists of two object detection networks to do temporal action detection. The first network is used to detect objects in each frame, and the second one is for temporal action detection. We also proposed a method which converts the object detection results of the first network into a new type of data so that it can be fed to the second network. The new type of data is an image of six channels with spatiotemporal information and is beneficial to temporal action detection. We conduct experiments on the dataset THUMOS14 which is used for temporal action detection and our approach achieves a satisfactory performance.