Vessel Re-identification and Activity Detection in Thermal Domain for Maritime Surveillance

• URL: http://arxiv.org/abs/2406.08294v1
• Date: Wed, 12 Jun 2024 14:57:37 GMT
• Title: Vessel Re-identification and Activity Detection in Thermal Domain for Maritime Surveillance
• Authors: Yasod Ginige, Ransika Gunasekara, Darsha Hewavitharana, Manjula Ariyarathne, Ranga Rodrigo, Peshala Jayasekara,
• Abstract summary: Vision-based maritime surveillance is challenging due to visibility issues at night. We introduce a thermal, vision-based approach for maritime surveillance with object tracking, vessel re-identification, and suspicious activity detection capabilities. This dataset will be the first publicly available benchmark dataset for thermal maritime surveillance.
• Score: 1.3649912426141015
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Maritime surveillance is vital to mitigate illegal activities such as drug smuggling, illegal fishing, and human trafficking. Vision-based maritime surveillance is challenging mainly due to visibility issues at night, which results in failures in re-identifying vessels and detecting suspicious activities. In this paper, we introduce a thermal, vision-based approach for maritime surveillance with object tracking, vessel re-identification, and suspicious activity detection capabilities. For vessel re-identification, we propose a novel viewpoint-independent algorithm which compares features of the sides of the vessel separately (separate side-spaces) leveraging shape information in the absence of color features. We propose techniques to adapt tracking and activity detection algorithms for the thermal domain and train them using a thermal dataset we created. This dataset will be the first publicly available benchmark dataset for thermal maritime surveillance. Our system is capable of re-identifying vessels with an 81.8% Top1 score and identifying suspicious activities with a 72.4\% frame mAP score; a new benchmark for each task in the thermal domain.

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