Multiple Ship Tracking in Remote Sensing Images Using Deep Learning
| Autor: | Qifan Wu, Zhejun Feng, Ziqiang Huang, Changqing Cao, Yuedong Zhou, Jin Wu, Xiaodong Zeng |
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| Rok vydání: | 2021 |
| Předmět: |
Similarity (geometry)
Generalization business.industry Computer science Science Deep learning Detector Feature extraction multiple granularity network (MGN) deep learning multi-object tracking remote sensing image Frame rate Tracking (particle physics) General Earth and Planetary Sciences Granularity Artificial intelligence business Remote sensing |
| Zdroj: | Remote Sensing, Vol 13, Iss 3601, p 3601 (2021) Remote Sensing; Volume 13; Issue 18; Pages: 3601 |
| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs13183601 |
| Popis: | In remote sensing images, small target size and diverse background cause difficulty in locating targets accurately and quickly. To address the lack of accuracy and inefficient real-time performance of existing tracking algorithms, a multi-object tracking (MOT) algorithm for ships using deep learning was proposed in this study. The feature extraction capability of target detectors determines the performance of MOT algorithms. Therefore, you only look once (YOLO)-v3 model, which has better accuracy and speed than other algorithms, was selected as the target detection framework. The high similarity of ship targets will cause poor tracking results; therefore, we used the multiple granularity network (MGN) to extract richer target appearance information to improve the generalization ability of similar images. We compared the proposed algorithm with other state-of-the-art multi-object tracking algorithms. Results show that the tracking accuracy is improved by 2.23%, while the average running speed is close to 21 frames per second, meeting the needs of real-time tracking. |
| Databáze: | OpenAIRE |
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