Object detection and movement tracking using tubelets and faster RCNN algorithm with anchor generation
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....
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my.um.eprints.280262022-07-15T07:18:01Z http://eprints.um.edu.my/28026/ Object detection and movement tracking using tubelets and faster RCNN algorithm with anchor generation Mohandas, Prabu Anni, Jerline Sheebha Thanasekaran, Rajkumar Hasikin, Khairunnisa Azizan, Muhammad Mokhzaini QA75 Electronic computers. Computer science TA Engineering (General). Civil engineering (General) 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. Wiley 2021-08-10 Article PeerReviewed 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 <https://doi.org/10.1155/2021/8665891>. 10.1155/2021/8665891 |
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QA75 Electronic computers. Computer science TA Engineering (General). Civil engineering (General) Mohandas, Prabu Anni, Jerline Sheebha Thanasekaran, Rajkumar Hasikin, Khairunnisa Azizan, Muhammad Mokhzaini Object detection and movement tracking using tubelets and faster RCNN algorithm with anchor generation |
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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. |
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Mohandas, Prabu Anni, Jerline Sheebha Thanasekaran, Rajkumar Hasikin, Khairunnisa Azizan, Muhammad Mokhzaini |
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Mohandas, Prabu Anni, Jerline Sheebha Thanasekaran, Rajkumar Hasikin, Khairunnisa Azizan, Muhammad Mokhzaini |
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Mohandas, Prabu |
title |
Object detection and movement tracking using tubelets and faster RCNN algorithm with anchor generation |
title_short |
Object detection and movement tracking using tubelets and faster RCNN algorithm with anchor generation |
title_full |
Object detection and movement tracking using tubelets and faster RCNN algorithm with anchor generation |
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Object detection and movement tracking using tubelets and faster RCNN algorithm with anchor generation |
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Object detection and movement tracking using tubelets and faster RCNN algorithm with anchor generation |
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object detection and movement tracking using tubelets and faster rcnn algorithm with anchor generation |
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Wiley |
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2021 |
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http://eprints.um.edu.my/28026/ |
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13.160551 |