Recent advances and trends in visual tracking: A review

By Hanxuan Yang, Ling Shao, Feng Zheng, Liang Wang, Zhan Song. Neurocomputing, 2011

650 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/2bcf2bd59219d89f335cbc8d1dd4f431076b4c4c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

Citations in the corpus

MUlti-Store Tracker (MUSTer): A cognitive psychology inspired approach to object tracking (2015, background): “A large number of trackers have been proposed [30, 49], and the recent publication of benchmark datasets containing large numbers of sequences and standardized quantitative evaluation metrics is accelerating the pace of development in this field [24, 43, 48].”
Attentional Correlation Filter Network for Adaptive Visual Tracking (2017, background): “Although visual tracking research has achieved remarkable advances in the past decades [21–23,32,38–40], and thanks to deep learning especially in the recent years [6,8,29,35,36,41], most methods employ only a subset of these properties, or are too slow to perform in real-time.”
Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning (2017, background): “As surveyed in [41, 29], various trackers have shown their performance and effectiveness on various tracking benchmarks [20, 18, 40].”
Fast Online Object Tracking and Segmentation: A Unifying Approach (2018, background): “Given the location of an arbitrary target of interest in the first frame of a video, the aim of visual object tracking is to estimate its position in all the subsequent frames with the best possible accuracy [69, 65, 55].”
Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Object Tracking (2018, background): “For a comprehensive review of existing tracking methods the reader can refer to recent surveys [1], [2], [3], [4], [5], [6].”
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).”
High-Speed Tracking with Kernelized Correlation Filters (2014, neutral): “The task of tracking, a crucial component of many computer vision systems, can be naturally specified as an online learning problem [1], [2].”