Tracking in Low Frame Rate Video: A Cascade Particle Filter with Discriminative Observers of Different Lifespans

By Yuan Li, Haizhou Ai, Takayoshi Yamashita, Shihong Lao, M. Kawade. 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007

324 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/058d0f273d7a607efc5f477e3ff8a802c95019b9

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

An online learned CRF model for multi-target tracking (2012, methodology): “Most visual tracking methods focus on tracking single object or multiple objects separately [9, 16]; they usually try to find proper appearance models that distinguish one object with all other targets or backgrounds, and adopt meanshift [4] or particle filtering [6] like approach to online adjust t”
Transfer Learning Based Visual Tracking with Gaussian Processes Regression (2014, background): “i) Some studies [14,36,23] observe that straightforward and frequent update of new observations may cause gradual drifting due to accumulated errors and loss of sample diversity.”
Multi-target tracking by learning local-to-global trajectory models (2015, background): “And last but not least, it is difficult to distinguish targets with similar appearances, especially when they are close to, or partially occlude each other.”
Semi-supervised On-Line Boosting for Robust Tracking (2008, background): “Recently, this has also been investigated by Li et al. [15] for tracking in low-frame rates.”
Survey on Contemporary Remote Surveillance Systems for Public Safety (2010, background): “The majority of existing approaches cannot be readily applied to LFR tracking problems because of their vulnerability to motion and appearance discontinuity inflicted by LFR data [64].”
Face tracking and recognition with visual constraints in real-world videos (2008, background): “For instance, [14] tackled the challenging case of the low frame rate videos by integrating tracking and detection in a cascade fashion, where the lifespan and feature sets of observation models change during the tracking process to increase efficiency and robustness of the tracker.”
Robust Object Tracking via Sparse Collaborative Appearance Model (2014, background): “, Kalman filter [1] and particle filter [2], [3]).”
Tracking-Learning-Detection (2012, methodology): “Such techniques have been applied to tracking of faces in lowframe-rate video [52], multiple hockey players [53], or pedestrians [54], [55].”