Comparative Analysis of Fine-Tuning I3D and SlowFast Networks for Action Recognition in Surveillance Videos
Human Action Recognition is considered to be a critical problem and it is always a challenging issue in computer vision applications, especially video surveillance applications. State-of-the-art classifiers introduced to solve the problem are computationally expensive to train and require very large...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
|
| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/59/1/203 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849341826374303744 |
|---|---|
| author | T. Gopalakrishnan Naynika Wason Raguru Jaya Krishna Vamshi Krishna B N. Krishnaraj |
| author_facet | T. Gopalakrishnan Naynika Wason Raguru Jaya Krishna Vamshi Krishna B N. Krishnaraj |
| author_sort | T. Gopalakrishnan |
| collection | DOAJ |
| description | Human Action Recognition is considered to be a critical problem and it is always a challenging issue in computer vision applications, especially video surveillance applications. State-of-the-art classifiers introduced to solve the problem are computationally expensive to train and require very large amounts of data. In this paper, we solve the problems of low data and resource availability in surveillance datasets by employing transfer learning and fine-tuning the Inflated 3D CNN model and the SlowFast Network model to automatically extract features from surveillance videos in the SPHAR dataset for classification into respective action classes. This approach works well to process the spatio-temporal nature of videos. Fine-tuning is carried out in the networks by replacing the last classification (dense) layer as per the available number of classes in the constructed new dataset. We ultimately compare the performance of both fine-tuned networks by taking accuracy as the metric, and find that the I3D model performs better for our use-case. |
| format | Article |
| id | doaj-art-7957dfc3050f472394bbbdf2f2c24f7b |
| institution | Kabale University |
| issn | 2673-4591 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-7957dfc3050f472394bbbdf2f2c24f7b2025-08-20T03:43:33ZengMDPI AGEngineering Proceedings2673-45912024-01-0159120310.3390/engproc2023059203Comparative Analysis of Fine-Tuning I3D and SlowFast Networks for Action Recognition in Surveillance VideosT. Gopalakrishnan0Naynika Wason1Raguru Jaya Krishna2Vamshi Krishna B3N. Krishnaraj4Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal 576104, Karnataka, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, IndiaDepartment of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal 576104, Karnataka, IndiaDepartment of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal 576104, Karnataka, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, IndiaHuman Action Recognition is considered to be a critical problem and it is always a challenging issue in computer vision applications, especially video surveillance applications. State-of-the-art classifiers introduced to solve the problem are computationally expensive to train and require very large amounts of data. In this paper, we solve the problems of low data and resource availability in surveillance datasets by employing transfer learning and fine-tuning the Inflated 3D CNN model and the SlowFast Network model to automatically extract features from surveillance videos in the SPHAR dataset for classification into respective action classes. This approach works well to process the spatio-temporal nature of videos. Fine-tuning is carried out in the networks by replacing the last classification (dense) layer as per the available number of classes in the constructed new dataset. We ultimately compare the performance of both fine-tuned networks by taking accuracy as the metric, and find that the I3D model performs better for our use-case.https://www.mdpi.com/2673-4591/59/1/203human action recognitionfine-tuningdeep learningsurveillanceconvolutional neural networkSPHAR |
| spellingShingle | T. Gopalakrishnan Naynika Wason Raguru Jaya Krishna Vamshi Krishna B N. Krishnaraj Comparative Analysis of Fine-Tuning I3D and SlowFast Networks for Action Recognition in Surveillance Videos Engineering Proceedings human action recognition fine-tuning deep learning surveillance convolutional neural network SPHAR |
| title | Comparative Analysis of Fine-Tuning I3D and SlowFast Networks for Action Recognition in Surveillance Videos |
| title_full | Comparative Analysis of Fine-Tuning I3D and SlowFast Networks for Action Recognition in Surveillance Videos |
| title_fullStr | Comparative Analysis of Fine-Tuning I3D and SlowFast Networks for Action Recognition in Surveillance Videos |
| title_full_unstemmed | Comparative Analysis of Fine-Tuning I3D and SlowFast Networks for Action Recognition in Surveillance Videos |
| title_short | Comparative Analysis of Fine-Tuning I3D and SlowFast Networks for Action Recognition in Surveillance Videos |
| title_sort | comparative analysis of fine tuning i3d and slowfast networks for action recognition in surveillance videos |
| topic | human action recognition fine-tuning deep learning surveillance convolutional neural network SPHAR |
| url | https://www.mdpi.com/2673-4591/59/1/203 |
| work_keys_str_mv | AT tgopalakrishnan comparativeanalysisoffinetuningi3dandslowfastnetworksforactionrecognitioninsurveillancevideos AT naynikawason comparativeanalysisoffinetuningi3dandslowfastnetworksforactionrecognitioninsurveillancevideos AT ragurujayakrishna comparativeanalysisoffinetuningi3dandslowfastnetworksforactionrecognitioninsurveillancevideos AT vamshikrishnab comparativeanalysisoffinetuningi3dandslowfastnetworksforactionrecognitioninsurveillancevideos AT nkrishnaraj comparativeanalysisoffinetuningi3dandslowfastnetworksforactionrecognitioninsurveillancevideos |