An Improved TLD Tracking Algorithm for Fast-moving Object

DOI

10.2991/csece-18.2018.15How to use a DOI?

Keywords

object tracking; TLD, real-time; narrowing region; variance threshold

Abstract

Traditional object tracking is easily affected by deformation, scale changes, illumination changes, partial occlusions and so on. TLD(Tracking-Learning-Detection) is a classic effective algorithm in long-term tracking which can solve these problems well. Meanwhile, the real-time performance of the system should be taken into account while in the actual situation. An improved fast-moving object tracking algorithm based on TLD is proposed in this paper. In the paper, a method of narrowing the region of detection is proposed to effectively minimize the consumption of time, the method is combined with self-prediction of motion direction to ensure the accuracy of detection. To compensate for the possible missing and false detections caused by the reduction of detection region and the changing background, the variance threshold is updated dynamically to let more possible correct bounding boxes pass the variance classifier. Experiments have been conducted to verify the improved TLD algorithm, the results show that our algorithm ensures the accuracy of object tracking and has a good performance on the real-time.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shijie Zhou
AU  - Yuanxi Peng
AU  - Kecheng Gong
AU  - Leizhi Shu
PY  - 2018/02
DA  - 2018/02
TI  - An Improved TLD Tracking Algorithm for Fast-moving Object
BT  - Proceedings of the 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)
PB  - Atlantis Press
SP  - 69
EP  - 73
SN  - 2352-538X
UR  - https://doi.org/10.2991/csece-18.2018.15
DO  - 10.2991/csece-18.2018.15
ID  - Zhou2018/02
ER  -