Robust Visual Tracking and Vehicle Classification via Sparse Representation

By Xue Mei, Haibin Ling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011

910 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1183db5f409e8498d1a0f542703f908275a6dc34

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Citations in the corpus

Precise and efficient model-based vehicle tracking method using Rao-Blackwellized and scaling series particle filters (2016, methodology): “[8] and a variety of PF-based tracking methods have been developed [9], [10], [11], [12], [13], [14], [15].”
Object Tracking Benchmark (2015, methodology): “Mei and Ling [53], [54] used a dictionary of holistic intensity templates composed of target and trivial templates, and determined the target location by solving multiple ‘1 minimization problems.”
Self-Paced Learning for Long-Term Tracking (2013, background): “Generative subspace models of appearance are common [12, 13], including recent work that makes use of sparse reconstructions [14, 15].”
Multi-task Sparse Learning with Beta Process Prior for Action Recognition (2013, background): “In the past few years, sparse representation has been successfully applied to computer vision applications, such as object classification [10], tracking [11], face recognition [5], and action recognition [6].”
Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-free Approach (2018, methodology): “SRC has been also applied to signal classification [8], visual tracking [15], and visual classification [26], etc.”
Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis (2013, background): “monocular tracking in the image plane [28], [31], [49], [73], [75], [144].”
MEEM: Robust Tracking via Multiple Experts Using Entropy Minimization (2014, methodology): “In [31,21], templates with the least importance will be replaced when they cannot well represent the target appearance.”
Fast Visual Tracking via Dense Spatio-temporal Context Learning (2014, methodology): “Numerous algorithms have been proposed with focus on effective appearance models, which are based on the target appearance [8,1,28,22,17,18,19,23,21,31] or the difference between appearances of the target and its local background [11,16,14,2,30,15].”
Transfer Learning Based Visual Tracking with Gaussian Processes Regression (2014, background): “, a set of basis vectors based on subspace learning [29,12] or least soft-threshold squares linear regression [32], a series of raw pixel templates based on sparse coding [25,24,44,43,36] or non-sparse linear representation [22]; ii) collaboration of multiple tracking models, e.”
Parallel Coordinate Descent Newton Method for Efficient $\ell_1$-Regularized Minimization (2013, background): “…arise in a wide range of applications including sparse logistic regression (Ng, 2004), 1 -regularized 2 -loss SVM (Chang et al., 2008), compressed sensing (Li and Osher, 2009), image coding (Lee et al., 2009), face recognition (Wright et al., 2009), and object tracking (Mei and Ling, 2011).”
Visual Tracking With Weighted Adaptive Local Sparse Appearance Model via Spatio-Temporal Context Learning (2018, background): “An effective appearance model plays a key role in ensuring the robustness of a tracking system, thereby attracting much attention in recent years [9], [11]–[25].”
Tracking via Robust Multi-task Multi-view Joint Sparse Representation (2013, methodology): “Sparse representation was recently introduced for tracking in [19] which casts tracking as a sparse representation problem in a particle filter framework [11] which was later exploited in [15][16][20].”
Real-Time Compressive Tracking (2012, methodology): “Second, our algorithm extracts a linear combination of generalized Haar-like features but these trackers [10][9] use the holistic templates for sparse representation which are less robust as demonstrated in our experiments.”
MUlti-Store Tracker (MUSTer): A cognitive psychology inspired approach to object tracking (2015, background): “[37] introduced sparse representations for tracking, subsequently adopted in many trackers [19, 51], in which the memory of the target appearance is modeled using a small set of target instances.”
Robust Visual Tracking via Convolutional Networks Without Training (2015, background): “Although much progress has been made in recent years [1]–[6], it remains a challenging task due to many factors such as illumination changes, partial occlusion, deformation, as well as viewpoint variation [7].”
A Survey of Sparse Representation: Algorithms and Applications (2015, methodology): “employed the idea of sparse representation to visual tracking [200] and vehicle classification [201], which introduced nonnegative sparse constraints and dynamic template updating strategy.”
Robust visual tracking via multi-task sparse learning (2012, methodology): “Recently, sparse representation [5] has been successfully applied to visual tracking [15, 14].”