Depth tracking of occluded ships based on SIFT feature matching

• Yadong Liu,
• Yuesheng Liu,
• Ziyang Zhong,
• Yang Chen,
• Jinfeng Xia,
• Yunjie Chen,

Vol. 17, No. 4, April 30, 2023
10.3837/tiis.2023.04.002, Download Paper (Free):

• multi-target tracking
• DeepSort
• SIFT
• Feature Points
• Deep Learning

Abstract

Multi-target tracking based on the detector is a very hot and important research topic in target tracking. It mainly includes two closely related processes, namely target detection and target tracking. Where target detection is responsible for detecting the exact position of the target, while target tracking monitors the temporal and spatial changes of the target. With the improvement of the detector, the tracking performance has reached a new level. The problem that always exists in the research of target tracking is the problem that occurs again after the target is occluded during tracking. Based on this question, this paper proposes a DeepSORT model based on SIFT features to improve ship tracking. Unlike previous feature extraction networks, SIFT algorithm does not require the characteristics of pre-training learning objectives and can be used in ship tracking quickly. At the same time, we improve and test the matching method of our model to find a balance between tracking accuracy and tracking speed. Experiments show that the model can get more ideal results.

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