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http://hdl.handle.net/10397/95952
Title: | Boosting the performance of scene recognition via offline feature-shifts and search window weights | Authors: | Li, CT Siu, WC Lun, DPK |
Issue Date: | Nov-2018 | Source: | 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), 19-21 November 2018, p. 1-5 | Abstract: | This paper presents a key frame recognition algorithm, using novel offline feature-shifts approach and search window weights. We extract effective feature patches from key frames with an offline feature-shifts approach for real-time key frame recognition. We focus on practical situations in which blurring and shifts in viewpoints occur in our dataset. We compare our method with some conventional keypoint-based matching methods and the newest CNN features for scene recognition. The experimental results illustrate that our method can reasonably preserve the performance in key frame recognition when comparing with methods using online feature-shifts approach. Our proposed method provides larger tolerance of unmatched pairs which is useful for decision making in real-time systems. Moreover, our method is robust to illumination and blurring. We achieve 90% accuracy in a nighttime sequence while CNN approach only attains 60% accuracy. Our method only requires 33.8 ms to match a frame on average using a regular desktop, which is 4 times faster than CNN approach with only CPU mode. | Keywords: | Key frame identification Vehicle detection Autonomous driving Visual place and key frame recognition |
Publisher: | IEEE | ISBN: | 978-1-5386-6811-5 (Electronic) 978-1-5386-6810-8 (USB) 978-1-5386-6812-2 (Print on Demand(PoD)) |
DOI: | 10.1109/ICDSP.2018.8631883 | 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 following publication C. -T. Li, W. -C. Siu and D. P. K. Lun, "Boosting the Performance of Scene Recognition via Offline Feature-Shifts and Search Window Weights," 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), 2018, pp. 1-5 is available at https://dx.doi.org/10.1109/ICDSP.2018.8631883. |
Appears in Collections: | Conference Paper |
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File | Description | Size | Format | |
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EIE-0440_Li_Boosting_Performance_Scene.pdf | Pre-Published version | 1.09 MB | Adobe PDF | View/Open |
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