Frame-Subsampled, Drift-Resilient Video Object Tracking
Xuan Wang, Yuhen Hu, Robert G. Radwin, John D. Lee
Abstract
Performance-cost trade-offs in video object tracking tasks for long video sequences is investigated. A novel frame-subsampled, drift-resilient (FSDR) video object tracking algorithm is presented that would achieve desired tracking accuracy while dramatically reducing computing time by processing only sub-sampled video frames. A new pattern matching score metric is proposed to estimate the probability of drifting. A drift-recovery procedure is developed to enable the algorithm to recover from a drift situation and resume accurate tracking. Compared against state-of-the-art video object tracking algorithms, dramatic performance (accuracy) enhancement and cost (computing time) reduction are observed.
BibTeX
@inproceedings{icassp2018_framesubsampledd,
title = {Frame-Subsampled, Drift-Resilient Video Object Tracking},
author = {Xuan Wang and Yuhen Hu and Robert G. Radwin and John D. Lee},
booktitle = {ICASSP 2018},
year = {2018}
}