A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf
.
STResNet_CF Tracker: The deep spatiotemporal features learning for correlation filter based robust visual object tracking
2019
IEEE Access
Constructing a robust appearance model of the visual object is a crucial task for visual object tracking. Recently, more and more studies combine spatial feature with a temporal feature to improve the tracking performance. These methods successfully apply the features from spatial and temporal to address the problem for tracking. This paper presents a novel method for visual object tracking based on spatiotemporal feature combined with correlation filters. In this paper, the visual features of
doi:10.1109/access.2019.2903161
fatcat:uas76gauobcspkjwvuvy6wpevi