Object detection in a maritime environment : performance evaluation of background subtraction methods

• Main Authors: Prasath, Chandrashekar Krishna, Rajan, Deepu, Rachmawati, Lily, Rajabally, Eshan, Quek, Chai, Prasad, Dilip Kumar
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2019
• Subjects: Engineering::Computer science and engineering; Autonomous Automobiles; Maritime Vehicles
• Online Access: https://hdl.handle.net/10356/84193; http://hdl.handle.net/10220/50180
• Institution: Nanyang Technological University

This paper provides a benchmark of the performance of 23 classical and state-of-the-art background subtraction (BS) algorithms on visible range and near infrared range videos in the Singapore Maritime dataset. Importantly, our study indicates the limitations of the conventional performance evaluation criteria for maritime vision and proposes new performance evaluation criteria that is better suited to this problem. This paper provides insight into the specific challenges of BS in maritime vision. We identify four open challenges that plague BS methods in maritime scenario. These include spurious dynamics of water, wakes, ghost effect, and multiple detections. Poor recall and extremely poor precision of all the 23 methods, which have been otherwise successful for other challenging BS situations, allude to the need for new BS methods custom designed for maritime vision.