Object tracking using SIFT features and mean shift

By Huiyu Zhou, Yuan Yuan, Chunmei Shi. Computer Vision and Image Understanding, 2009

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A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit (2021, methodology): “In [33], the SIFT keypoints and features are used to detect humans in images, and in [34] SIFT was combined with color histograms to classify regions of interest across frames to track objects in videos.”
A survey of appearance models in visual object tracking (2013, positive): “…2011] Global Active contour representation 15 [Lin et al. 2007] Local Local feature-based represnetation (local templates) 16 [Tang and Tao 2008; Zhou et al. 2009] Local Local feature-based represnetation (SIFT features) 17 [Donoser and Bischof 2006; Tran and Davis 2007] Local Local…”
Sparse coding based visual tracking: Review and experimental comparison (2013, background): “In the literature, a number of tracking algorithms have been proposed (for example [1–6]; see [7,8] for detailed reviews).”
Iterative Hierarchical Optimization for Misspecified Problems (IHOMP) (2016, background): “It is well established in the machine learning [Levi and Weiss, 2004, Zhou et al., 2009] and RL [Konidaris et al., 2011] literature that “good” features can have a dramatic impact on performance.”