Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Electronic and Information Engineering | en_US |
| dc.contributor.advisor | Lam, Kin Man (EIE) | en_US |
| dc.creator | Lai, Songjiang | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/12139 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Deep learning for human action recognition | en_US |
| dcterms.abstract | With the rapid development and wide popularity of deep learning in recent years, the performance of computer vision tasks has been greatly improved. The two-stream neural network model, applied to video-based action recognition, has now become a hot research topic. Similar to the traditional two-stream convolutional neural network model for action recognition, the inputs to the two branches are the RGB stream and the optical-flow stream, which can be used for human action recognition with promising performance. However, the two-stream model requires high computational complexity because computing optical flow from a video sequence is computationally intensive. Furthermore, the inputs for the two streams are different, i.e., RGB and optical flow. The original two-stream model cannot be trained end to end, which increases the complexity in training the model and limits the performance. In this research, we introduce a representation flow algorithm proposed by AJ al et[1]., based on the TV1-L1 [2] model, which is similar to the optical-flow algorithm. We replace the traditional optical flow branch of egocentric action recognition model proposed by Swathikiran et al. [3] with the representation-flow branch to make it an end-to-end trainable model. This can greatly reduce the computational cost and the prediction runtime of the new model. We apply the new two-stream model for egocentric action recognition. Moreover, we also apply the class attention maps (CAMs) to the RGB stream, so the model can pay more attention to those regions correlated with the activities under consideration. This can significantly improve the recognition accuracy. Then, we apply convLSTM for spatio-temporal encoding on the image features with spatial attention. We train and evaluate the proposed model on three different data sets: GTEA61, EGTEA GAZE+ and HMDB[4]. Experiment results show that our proposed model can achieve the same recognition accuracy as the original egorcnn model with an optical-flow branch on GTEA61 but outperforms it by 0.65% and 0.84% on EGTEA GAZE+ and HMDB, respectively. In terms of speed, experiment results show the average runtime of our proposed model is 0.1881s, 0.1503s, and 0.1459s on the GTEA61, EGTEA GAZE+ and HMDB databases, respectively, while the corresponding runtimes (including the time for extracting optical flow) for the original model are 101.6795s, 25.3799s, and 203.9958s, respectively. Finally, we also conduct ablation studies and discuss the influence of different parameters on the performance of our proposed model, such as the number of layers for representation flow, the different number of blocks for the backbone architecture, etc. | en_US |
| dcterms.extent | ix, 52 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2022 | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.LCSH | Computer vision -- Mathematical models | en_US |
| dcterms.LCSH | Human activity recognition | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 6523.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.21 MB | Adobe PDF | View/Open |
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