Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/84193
Title: Object detection in a maritime environment : performance evaluation of background subtraction methods
Authors: Prasath, Chandrashekar Krishna
Rajan, Deepu
Rachmawati, Lily
Rajabally, Eshan
Quek, Chai
Prasad, Dilip Kumar
Keywords: Engineering::Computer science and engineering
Autonomous Automobiles
Maritime Vehicles
Issue Date: 2018
Source: Prasad, D. K., Prasath, C. K., Rajan, D., Rachmawati, L., Rajabally, E., & Quek, C. (2019). Object detection in a maritime environment : performance evaluation of background subtraction methods. IEEE Transactions on Intelligent Transportation Systems, 20(5), 1787-1802. doi:10.1109/TITS.2018.2836399
Series/Report no.: IEEE Transactions on Intelligent Transportation Systems
Abstract: 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.
URI: https://hdl.handle.net/10356/84193
http://hdl.handle.net/10220/50180
ISSN: 1524-9050
DOI: http://dx.doi.org/10.1109/TITS.2018.2836399
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TITS.2018.2836399.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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