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Title: Semi-supervised deep vision-based localization using temporal correlation between consecutive frames
Author(s): Siu, Wan Chi 
Author(s): Li, C.-T.
Lun, D. P. K.
Issue Date: 2019
Publisher: IEEE
Related Publication(s): Proceedings of 2019 IEEE International Conference on Image Processing (ICIP)
Start page: 1985
End page: 1989
Vision-based localization is a temporal informative task in which we can obtain information about the ego-motion of a vehicle from the historical information via examining consecutive frames. Sufficient temporal information helps to reduce the search space of the next location. Hence, both efficiency and accuracy of the localization system can be enhanced. This paper presents a semi-supervised deep vision-based localization algorithm, using a novel tubing strategy to find the starting location of a vehicle. We group different number of consecutive frames as sets of tubes based on their temporal correlation to achieve pair searching with variable tube sizes. We also enhance an off-the-shelf network model with our modified training data generation method to improve the discrimination power of the features given by the model. Experimental results show that our proposed temporal correlation based initialization module can confidently localize the starting location of a vehicle (for a certain journey), and achieve 40% precision improvement over that of the conventional CNN approaches.
DOI: 10.1109/ICIP.2019.8803131
CIHE Affiliated Publication: No
Appears in Collections:CIS Publication

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