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Robust Visual Tracking – Algorithms, Evaluations and Problems

Robust Visual Tracking – Algorithms, Evaluations and Problems. Haibin Ling Department of Computer and Information Sciences Temple University Philadelphia, PA 19122. October 15, 2014. Visual Tracking. Continuously localization of a visual entity or visual entities.

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Robust Visual Tracking – Algorithms, Evaluations and Problems

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  1. Robust Visual Tracking – Algorithms, Evaluations and Problems Haibin Ling Department of Computer and Information Sciences Temple University Philadelphia, PA 19122 October 15, 2014

  2. Visual Tracking Continuously localization of a visual entity or visual entities. Single target tracking (model-free) (PAMI’11,CVPR’11,ICCV’11,CVPR’12,ICCV’13,ECCV’14) Pose tracking (Sigal et al 2004) Multi-target tracking (CVPR’13,CVPR’14a) Contour tracking (CVPR’14b)

  3. Visual Tracking Continuously localization of a visual entity or visual entities. Single target tracking (model-free) (PAMI’11,CVPR’11,ICCV’11,CVPR’12,ICCV’13,ECCV’14) Related work • Tooooooo many to be listed • A survey by Yilmaz, Javed & Shah in 2006 • There are many influential trackers after 2006

  4. Outline • Problem formulation and particle filter tracking framework • Visual tracking using sparse representation • Reducing bias in tracking evaluation • Recent and future work

  5. Problem formulation Input: • A sequence of images: I0, I1, …, It, … • Target of interest at the initial frame: x0 A target is represented by a state vector x = (pos, scale, orientation)‘ Output: • Targets in each of the following frames • x1, …, xt, …

  6. Tracking by Bayesian Estimation • Bayesian estimation: At frame t, find the best xtby Bayesian inference Using observations (features) extracted from images I0, I1, …, It : We have • Kalman filter • Gaussian everywhere  closed form solution  • But, probabilities in visual tracking is not usually Gaussian  • Particle filter • Probability propagation: iterative prediction and updating • Sampling techniques

  7. Particle Filter (Isard & Blake 98) • Visual tracking • Probability propagation Prediction: Update: • Particle sampling (sequential Monte Carlo) Approximate the posterior density by a set of weighted samples: • Now we need to decide

  8. Outline • Problem formulation and particle filter tracking framework • Visual tracking using sparse representation • Reducing bias in tracking evaluation • Recent and future work

  9. Motivation Intuition • During tracking, there is a large redundancy in the observation of target appearance • It is common to represent the target appearance using a linear representation Idea • Introduce sparse constraints in the linear target representation • Non-negativity constraints Advantage • Models observation redundancy naturally. • Addresses discrete appearance corruption such as occlusion (Wright et al. 2009) • Benefits from recent advance in solutions for sparse coding/compressive sensing (Candes et al. 2006, Donoho 2006) • A flexible framework (as illustrated in many extensions)

  10. Sparse Representation for Tracking A candidate y approximately lies in a linear subspace, which is spanned by templates from past observation • Rewrite as • Task: find a sparse solution for a and e.

  11. Non-negativity Constraints • In addition to the (positive) trivial templates I, we include negative trivial templates -I. where ai, ei, ei- >=0 . • The formula can be rewritten as

  12. Example Templates

  13. Comparing Good and Bad Candidates

  14. Achieving Sparse Solutions • Our task is to find a sparse solution to the following linear system, • It leads to an L0 minimization task, such as • This can be well approximated, under very flexible conditions, by an L1 minimization,

  15. Extension • Speed up • Speed up: bounded particle resampling (CVPR’11) • Speed up: accelerated proximal gradient (CVPR’12) • Blurred target tracking (ICCV’11) • Other sparse-representation trackers • Liu et al. ECCV'10, • Li, Shen & Shi CVPR'11, Liu et al CVPR'11, Kwak et al ICCV’11 • Zhong, Lu & Yang CVPR'12; Jia, Lu & Yang CVPR'12; Zhang, Zhang & Yang CVPR'12; ZhangT et al CVPR'12, • ZhangT et al IJCV’13, Huet al PAMI’14 • …

  16. Outline • Problem formulation and particle filter tracking framework • Visual tracking using sparse representation • Reducing bias in tracking evaluation • Recent and future work

  17. Reducing Subjective Bias • Which are the best trackers among all? • Implementing and testing on a large benchmark (e.g., Wu et al 2013) is a huge project. • Recent trend: compare the authors’ own tracker with many other trackers. • Their own tracker typically performs the best. • It has advantages that the authors want to highlight. • Optimizing all trackers is non-trivial, if not possible. • We aim to reduce such biases and provide a more practical comparison.

  18. An example Average Center Location Error The authors’ previous tracker The proposed tracker • The best two results are shown in red and blue

  19. Partial ranking representation Average Center Location Error A 17.5 B 56.7 D 10.5 < <

  20. Pairwise representation Average Center Location Error D 39.2 A 7.0 B 39.2 < = Seq 1 Seq 2 Seq N … (A, B, 1) (A, B, 1) (A, B, 1) (A, D, 1) (A, D, 1) (D, A, 1) (B, D, 0.5) (B, D, 1) (D, B, 1) (D, B, 0.5)

  21. Data Statistics • PAMI (2000 Vol.22– 2013 Vol.35), IJCV (2000 Vol.36 – 2013 Vol.104) • ICCV, CVPR, ECCV (2005 – 2013) • 45 papers (tournament) contain useful table data • 48 trackers appear in the data at the first stage • 15 trackers are left after the cleaning • 664 partial rankings • 6280 pairs of records with 151 draw records

  22. Paper selection and data cleaning • More than 2 trackers left after remove unqualified trackers • Independent assumption • Conference to journal extension • Duplicate experimental results • Significant lack of data • Compared only in one tournament • #records ≤ 10

  23. Rank aggregation • Rank aggregation (Ailon 2010) • Find a full-ranking to minimize the total violation of pairwise comparison. • NP-Hard, LpKwikSorth algorithm • PageRank-like ranking (Page et al. 1999) • Graph-based solution • Elo’s rating (Elo 1978) • Widely used in sport ranking (chess, football, …) • Sequentially update score based on each game • Glicko’s rating (Glickman 1999) • Extension of Elo’s rating by introducing confidence

  24. Ranking results

  25. Outline • Problem formulation and particle filter tracking framework • Visual tracking using sparse representation • Reducing bias in tracking evaluation • Recent and future work

  26. Tracking with GPR (TGPR) Transfer Learning Based Visual Tracking with Gaussian Processes Regression Gao, Ling, Hu & Xing, ECCV 2014 Source code of TGPR available: http://www.dabi.temple.edu/~hbling/code/TGPR.htm or http://jingao.weebly.com/

  27. Promising Results CVPR2013 Benchmark (Wu et al 2013) 50 sequences Princeton Benchmark (Song & Xiao 2013) 100 sequences VOT2013 (Kristan et al 2013) 16 sequences

  28. Acknowledgement • Collaborators ChenglongBao, Erik Blasch, Jin Gao, WeimingHu HuiJi, Xue Mei, Yu Pang, Yi Wu • Funding • National Sciences Foundation • Air Force Research Laboratory

  29. Thank You!&Questions?

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