Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/141797
Title: Real time multiple object tracking using deep features and localization information
Authors: Karunasekera, Hasith
Zhang, Handuo
Wang, Han
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Karunasekera, H., Zhang, H., & Wang, H. (2019). Real time multiple object tracking using deep features and localization information. Proceedings of 2019 IEEE 15th International Conference on Control and Automation (ICCA), 332-337. doi:10.1109/ICCA.2019.8899498
Project: MRP1A
Abstract: In this paper we propose a tracking by detection method using a dissimilarity measure calculated based on the location and the appearance information of the object. These dissimilarity values are used in Hungarian Algorithm [1] in the data association step for track identity assignment. We make use of YOLO [2] deep learning based object detector in the detection step from camera image feed. Location measure is calculated using the predicted object location and bounding box, while the appearance measure is from the last feature layer from the detection network. Main focus in this work is to propose a tracking framework that can be used in real time automated vehicle guiding applications, by striking a balance between computational complexity and tracking accuracy. Therefore, we make use of the deep features available from detection framework rather than calculating a new appearance measure during the tracking step. The method proposed is very efficient and enables to achieve speeds up to 500+ frames per second (fps) in KITTI [3] tracking benchmark while achieving state-of-the-art results.
URI: https://hdl.handle.net/10356/141797
ISBN: 9781728111650
DOI: 10.1109/ICCA.2019.8899498
Rights: © 2019 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/ICCA.2019.8899498.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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