Ensemble Tracking

By Shai Avidan. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005

1,589 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/82c0dba2a7a175113050a6c0dbd409c93cc48996

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In Artificial Intelligence

In Biomedical Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Autonomous flight in unstructured and unknown indoor environments (2009, methodology): “We developed a modified version of the classifier-based adaptiv e ensemble tracker, developed by Avidan [11].”
Robust Visual Tracking via Structured Multi-Task Sparse Learning (2012, methodology): “Examples of discriminative methods are on-line boosting (OAB) (Grabner et al. 2006), semi-online boosting (Grabner et al. 2008), ensemble tracking (Avidan 2005), co-training tracking (Liu et al. 2009), online multi-view forests for tracking (Leistner et al. 2010), adaptive metric differential…”
Forward-Backward Error: Automatic Detection of Tracking Failures (2010, background): “proaches [7, 4, 1, 2] on 6 video sequences from [14].”
Coupled Detection and Trajectory Estimation for Multi-Object Tracking (2007, methodology): “[20, 12, 2]), several recent approaches have started to explore the possibilities of combining tracking with detection [17, 1, 8, 22].”
Covariance Tracking using Model Update Based on Lie Algebra (2006, background): “Tracking can also be considered as a classification problem and a classifier can be trained to distinguish the object from the background [1].”
Robust object tracking via sparsity-based collaborative model (2012, background): “Numerous successful update approaches have been proposed [6, 10, 3, 21, 19].”
People-tracking-by-detection and people-detection-by-tracking (2008, background): “Tracking by detection has been a focus of recent work [18, 8, 27, 1].”
Robust tracking-by-detection using a detector confidence particle filter (2009, background): “In order to cope with those difficulties, tracking-by-detection approaches have become increasingly popular [2, 11, 16, 20, 26].”
Abnormal event detection in crowded scenes using sparse representation (2013, background): “Much progresses in video surveillance have been achieved in recent years for some key areas, such as background model [29], object tracking [2], pedestrian detection [8], action recognition [36], crowd counting [6] and traffic monitoring [32].”
On-line Boosting and Vision (2006, methodology): “A similar quasi on-line approach is used by Avidan for the application of visual tracking [5].”
Real-Time Tracking via On-line Boosting (2006, methodology): “In addition, to the on-line adaption problem, recently many techniques have addressed the idea of using information about the background in order to increase the robustness of tracking [1, 19, 2, 26, 6].”
Robust Multiperson Tracking from a Mobile Platform (2009, background): “In such cases, background subtraction [49], [52] is no longer a viable option and tracking-by-detection appears to be the most promising alternative [1], [2], [19], [21], [29], [32], [39], [57], [60].”
Visual tracking with online Multiple Instance Learning (2009, background): “the design of appearance models is whether to model only the object [5, 21], or both the object and the background [18, 14, 19, 4, 3, 24, 7].”
Robust tracking using local sparse appearance model and K-selection (2011, methodology): “Discriminative methods formulate tracking as a classification problem [3] [4] [6] [11].”
Watch and learn: Semi-supervised learning of object detectors from videos (2015, methodology): “Temporally consistent detections: We ﬁrst remove detections that are temporally inconsistent using a smoothness prior on the motion of detections.”
In defense of color-based model-free tracking (2015, background): “Due to the success of discriminative models, a large variety of suitable learning methods has been explored for visual tracking, such as structured output SVMs [21], ranking SVMs [5], boosting [3, 18], kernel ridge regression [22, 23], and multipleinstance learning [4].”
Robust Superpixel Tracking (2014, methodology): “The ensemble tracker [26] formulates the task as a pixel-based binary classification problem.”