An adaptive color-based particle filter

By Katja Nummiaro, Esther Koller-Meier, Luc Van Gool. Image and Vision Computing, 2003

1,287 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/e11dab9ddf9ec9b2a0fa35e4b91656ee2ad63aa0

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

In defense of color-based model-free tracking (2015, methodology): “[12, 34, 35]) were a common method for appearance description.”
A review of hand gesture and sign language recognition techniques (2017, methodology): “In research [133, 135, 138], tracking is performed using color features, and particle filtering has shown to be able to accurately track the movement of the gesture.”
A survey on player tracking in soccer videos (2017, methodology): “Appearance variations have also een handled through adaptive methods by adjusting size ( Qian t al., 2007 ) and color ( Montañés Laborda et al., 2011 ; Zheng and ue, 2009 ; Nummiaro et al., 2003 ; Wang et al., 2006 ).”
Adaptive Color Attributes for Real-Time Visual Tracking (2014, background): “While significant progress has been made to visual tracking, the use of color information is limited to simple color space transformations [19, 17, 18].”
Convolutional Features for Correlation Filter Based Visual Tracking (2015, methodology): “Initially, most tracking approaches relied on using only image intensity information or simple color transformations [4, 30, 32] for feature representation.”
Staple: Complementary Learners for Real-Time Tracking (2015, methodology): “While colour histograms were used in many early approaches to object tracking [32, 31], they have only recently been demonstrated to be competitive in modern benchmarks in the Distractor-Aware Tracker (DAT) [33], which uses adaptive thresholding and explicit suppression of regions with similar colou”
Robot Programming without Coding (2020, methodology): “The Bhattacharyya coefficient [24] used as a distance between two color distributions ν and b, is”
Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles (2008, methodology): “Similarly to that in [36], we compare color models by their Bhattacharyya coefficient:”
Structured Pruning of Deep Convolutional Neural Networks (2015, background): “Particle Filtering (PF) finds its applications in several fields [Arulampalam et al. 2002; Nummiaro et al. 2003; Nakamura et al. 2009; Carpenter et al. 1999].”
Object Detection and Tracking for Autonomous Navigation in Dynamic Environments (2010, methodology): “In order to have a richer description of the detected object region, the observation is often augmented with local image statistics, mostly color histograms (e.g. Nummiaro et al. 2003; Okuma et al. 2004; Wu and Nevatia 2007).”