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Intelligent Collaborative Tracking by Mining Auxiliary Objects

https://doi.org/10.1109/cvpr.2006.157
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/3-540-47977-5_20openalex-snapshot
On Affine Invariant Clustering and Automatic Cast Listing in Movies
inferredcites →https://doi.org/10.1007/bfb0015548openalex-snapshot
EigenTracking: Robust matching and tracking of articulated objects using a view-based representation
inferredcites →https://doi.org/10.1007/bfb0015549openalex-snapshot
Contour tracking by stochastic propagation of conditional density
inferredcites →https://doi.org/10.1109/cvpr.1996.517104openalex-snapshot
Real-time tracking of image regions with changes in geometry and illumination
inferredcites →https://doi.org/10.1109/cvpr.1998.698614openalex-snapshot
Elliptical head tracking using intensity gradients and color histograms
inferredcites →https://doi.org/10.1109/cvpr.2000.854761openalex-snapshot
Real-time tracking of non-rigid objects using mean shift
inferredcites →https://doi.org/10.1109/cvpr.2001.990517openalex-snapshot
Rapid object detection using a boosted cascade of simple features
inferredcites →https://doi.org/10.1109/cvpr.2004.1315071openalex-snapshot
Video data mining using con .gurations of viewpoint invariant regions
inferredcites →https://doi.org/10.1109/cvpr.2004.1315118openalex-snapshot
Collaborative tracking of multiple targets
inferredcites →https://doi.org/10.1109/cvpr.2005.144openalex-snapshot
Ensemble Tracking
inferredcites →https://doi.org/10.1109/cvpr.2005.260openalex-snapshot
Online Learning of Probabilistic Appearance Manifolds for Video-Based Recognition and Tracking
inferredcites →https://doi.org/10.1109/cvpr.2005.262openalex-snapshot
Online Selecting Discriminative Tracking Features Using Particle Filter
inferredcites →https://doi.org/10.1109/cvpr.2005.352openalex-snapshot
Tracking Non-Stationary Appearances and Dynamic Feature Selection
inferredcites →https://doi.org/10.1109/cvpr.2005.359openalex-snapshot
Unsupervised Learning of Object Features from Video Sequences
inferredcites →https://doi.org/10.1109/cvpr.2006.181openalex-snapshot
Measurement integration under inconsistency for robust tracking
inferredcites →https://doi.org/10.1109/iccv.1999.790410openalex-snapshot
Object recognition from local scale-invariant features
inferredcites →https://doi.org/10.1109/iccv.2001.937561openalex-snapshot
Indexing based on scale invariant interest points
inferredcites →https://doi.org/10.1109/iccv.2003.1238365openalex-snapshot
On-line selection of discriminative tracking features
inferredcites →https://doi.org/10.1109/iccv.2003.1238663openalex-snapshot
Video Google: a text retrieval approach to object matching in videos
inferredcites →https://doi.org/10.1109/tkde.2005.166openalex-snapshot
Fast algorithms for frequent itemset mining using FP-trees
inferredcites →https://doi.org/10.1109/tpami.2004.53openalex-snapshot
Support vector tracking

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-642-37431-9_7openalex-snapshot
Structured Visual Tracking with Dynamic Graph
inferred← citeshttps://doi.org/10.1007/s00371-020-01848-yopenalex-snapshot
A survey on online learning for visual tracking
inferred← citeshttps://doi.org/10.1016/j.cviu.2008.11.003openalex-snapshot
Probabilistic fusion-based parameter estimation for visual tracking
inferred← citeshttps://doi.org/10.1016/j.cviu.2015.06.011openalex-snapshot
People re-identification across non-overlapping cameras using group features
inferred← citeshttps://doi.org/10.1016/j.cviu.2017.05.010openalex-snapshot
Multi-object tracking through learning relational appearance features and motion patterns
inferred← citeshttps://doi.org/10.1016/j.imavis.2013.10.006openalex-snapshot
Tracking in dense crowds using prominence and neighborhood motion concurrence
inferred← citeshttps://doi.org/10.1016/j.neucom.2011.07.024openalex-snapshot
Recent advances and trends in visual tracking: A review
inferred← citeshttps://doi.org/10.1049/iet-cvi.2013.0134openalex-snapshot
Survey of single‐target visual tracking methods based on online learning
inferred← citeshttps://doi.org/10.1109/cca.2009.5281116openalex-snapshot
Robust visual tracking based on selective attention shift
inferred← citeshttps://doi.org/10.1109/icassp.2013.6637960openalex-snapshot
Robust object tracking using a context based on the relation of object and background
inferred← citeshttps://doi.org/10.1109/icip.2007.4379285openalex-snapshot
Collaborative Mean Shift Tracking Based on Multi-Cue Integration and Auxiliary Objects
inferred← citeshttps://doi.org/10.1109/icip.2007.4379321openalex-snapshot
Mining Auxiliary Objects for Tracking by Multibody Grouping
inferred← citeshttps://doi.org/10.1109/icnc.2010.5584296openalex-snapshot
Visual attention & multi-cue fusion based human motion tracking method
inferred← citeshttps://doi.org/10.1109/ivsurv.2011.6157020openalex-snapshot
A brief review on visual tracking methods
inferred← citeshttps://doi.org/10.1109/sice.2008.4655180openalex-snapshot
Bayesian fusion of laser and vision for multiple People Detection and tracking
inferred← citeshttps://doi.org/10.1109/tip.2014.2302675openalex-snapshot
Multilabel Image Classification Via High-Order Label Correlation Driven Active Learning
inferred← citeshttps://doi.org/10.1109/tpami.2008.146openalex-snapshot
Context-Aware Visual Tracking
inferred← citeshttps://doi.org/10.1145/3309665openalex-snapshot
Handcrafted and Deep Trackers
inferred← citeshttps://doi.org/10.48550/arxiv.1812.07368openalex-snapshot
Handcrafted and Deep Trackers: Recent Visual Object Tracking Approaches and Trends
inferred← citeshttps://doi.org/10.5244/c.22.93openalex-snapshot
Multi-target Data Association by Tracklets with Unsupervised Parameter Estimation
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