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

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

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

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

Problem formulation Input: A sequence of images: I 0, I 1, …, I t, … Target of interest at the initial frame: x 0 A target is represented by a state vector x = (pos, scale, orientation)‘ Output: Targets in each of the following frames – x 1, …, x t, …

Tracking by Bayesian Estimation At frame t, find the best x t by Bayesian inference Using observations (features) extracted from images I 0, I 1, …, I t : 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 Bayesian estimation:

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

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

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)

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

Non-negativity Constraints In addition to the (positive) trivial templates I, we include negative trivial templates -I. where a i, e i, e i - >=0.  The formula can be rewritten as

Example Templates

Comparing Good and Bad Candidates

Achieving Sparse Solutions  Our task is to find a sparse solution to the following linear system,  It leads to an L 0 minimization task, such as  This can be well approximated, under very flexible conditions, by an L 1 minimization,

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, Hu et al PAMI’14 – …

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

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.

An example ABCDE Seq Seq ……………… Seq N The best two results are shown in red and blue Average Center Location Error The proposed tracker The authors’ previous tracker

Partial ranking representation ABCDE Seq Seq ……………… Seq N Average Center Location Error Higher rankLower rank D<A<B Higher rankLower rank D<A<B A<B=D …<…<… A<B<D D 10.5 A 17.5 B 56.7 <<

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

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

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

Rank aggregation Rank aggregation (Ailon 2010) – Find a full-ranking to minimize the total violation of pairwise comparison. – NP-Hard, LpKwikSort h 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

Ranking results

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

Tracking with GPR (TGPR) Transfer Learning Based Visual Tracking with Gaussian Processes Regression Gao, Ling, Hu & Xing, ECCV 2014 Source code of TGPR available: or

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

Acknowledgement Collaborators Chenglong Bao, Erik Blasch, Jin Gao, Weiming Hu Hui Ji, Xue Mei, Yu Pang, Yi Wu Funding National Sciences Foundation Air Force Research Laboratory

Thank You! & Questions?