Mohandas, Prabu and Anni, Jerline Sheebha and Thanasekaran, Rajkumar and Hasikin, Khairunnisa and Azizan, Muhammad Mokhzaini (2021) Object detection and movement tracking using tubelets and faster RCNN algorithm with anchor generation. Wireless Communications and Mobile Computing, 2021. ISSN 1530-8669, DOI https://doi.org/10.1155/2021/8665891.
Full text not available from this repository.Abstract
Object detection in images and videos has become an important task in computer vision. It has been a challenging task due to misclassification and localization errors. The proposed approach explored the feasibility of automated detection and tracking of elephant intrusion along forest border areas. Due to an alarming increase in crop damages resulted from movements of elephant herds, combined with high risk of elephant extinction due to human activities, this paper looked into an efficient solution through elephant's tracking. The convolutional neural network with transfer learning is used as the model for object classification and feature extraction. A new tracking system using automated tubelet generation and anchor generation methods in combination with faster RCNN was developed and tested on 5,482 video sequences. Real-time video taken for analysis consisted of heavily occluded objects such as trees and animals. Tubelet generated from each video sequence with intersection over union (IoU) thresholds have been effective in tracking the elephant object movement in the forest areas. The proposed work has been compared with other state-of-the-art techniques, namely, faster RCNN, YOLO v3, and HyperNet. Experimental results on the real-time dataset show that the proposed work achieves an improved performance of 73.9% in detecting and tracking of objects, which outperformed the existing approaches.
| Item Type: | Article |
|---|---|
| Funders: | Department of Science & Technology (DOST), Philippines, Department of Science & Technology (India) |
| Uncontrolled Keywords: | Object detection; Movement tracking; Tubelets; Faster RCNN algorithm; Anchor generation |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 15 Jul 2022 07:18 |
| Last Modified: | 15 Jul 2022 07:18 |
| URI: | http://eprints.um.edu.my/id/eprint/28026 |
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