1. Orderless Tracking through Model-Averaged Posterior Estimation Seunghoon Hong* Suha Kwak Bohyung Han Computer Vision Lab. Dept. of Computer Science and Engineering POSTECH

2.  Robust estimation of accurate target states through out an input video Goal of Visual Tracking

3. State Space  Posterior estimation for the target states Image Space x y s x y s x y s Bayesian Tracking Approach

4. Conventional Tracking Approaches 129 34 43 50 92  Sequential estimation of the target posteriors by a temporal order of frames

5. Our Approach  Tracking easy-to-track frames first by searching a suitable order of frames 129 34 43 50 92

6. Overall Framework …… 1 1 2 2 3 3

7. Challenges Challenge 2 : How to select the easiest frame to track next ? Challenge 1 : How to propagate posterior between arbitrary frames ? …… 1 1 2 2 3 3

8. Density Propagation  Density propagation by Bayesian Model Averaging : tracked frames : a remaining frame Prior for each chain model Posterior propagation along each chain model

9. Matched patches Vote to center … [1] S. Korman and S. Avidan. Coherency sensitive hashing. In ICCV, 2011 Patch matching [1] and voting process Sample 1Voting map by sample 1

10.  Aggregate all voting maps from samples, and generate propagated posterior. … Sample 2 Sample 3Sample 4 …

11.  Which frame is preferred to track next? x y s x y s x y s CLEAR Unique and certain mode in distribution AMBIGUOUS Unique but uncertain mode in distribution AMBIGUOUS uncertain and multi- modal distribution Remaining frame #1 Remaining frame #2 Remaining frame #3 Identifying Subsequent Frame

12.  Measuring uncertainty by entropy Divide state space into regular grid blocks Target densityMarginalize the density per block Identifying Subsequent Frame

13. 1. Calculate entropy for all remaining frames 2. Select the subsequent frame with minimum entropy 4. Update the lists of tracked and remaining frames 3. Infer the target location in the newly tracked frame Identifying Subsequent Frame

14. Temporal order Identified Tracking Order  Occlusions Identified tracking order

15. Identified Tracking Order Temporal order  Shot changes Identified tracking order

16. Computational Complexity

17. Key frame selection Propagate posteriors to the remaining frames Efficient Hierarchical Approach Tracking key frames by the proposed tracker

18. Key-frame Selection Dissimilarities between all pairs of frames Geodesic distances from a sparse nearest neighbor graph ISOMAP Embedded frames

19. Density propagation by Patch-matching and voting Posterior Propagation from Key to Non-key frames

20. Quantitative Results Bounding box overlap ratio Center location error IVT SCML1APGASLSAFRAGWLMCOTLEOMASMA animal10.616.648.8179.694.164.819.47.77.4 TUD12.612.27.467.217.368.227.44.45.9 campus38.712.216.112.23.313.55.83.27.0 accident27.63.020.32.97.412.29.12.66.5 tennis68.765.985.068.867.431.037.06.911.9 boxing128.196.0114.6106.880.011.741.710.522.6 youngki95.2115.0137.9151.897.516.015.711.414.0 skating77.849.4143.922.835.414.718.38.010.8 psy156.5212.371.8146.895.266.061.215.021.9 Dance2283.9208.0113.9118.1132.439.7118.815.119.7 IVT SCML1APGASLSAFRAGWLMCOTLEOMASMA animal0.600.550.400.040.080.310.480.71 TUD0.650.670.850.320.590.380.480.820.75 campus0.560.620.520.630.770.520.720.780.67 accident0.580.870.690.840.600.570.590.850.76 tennis0.060.110.290.120.110.430.310.630.56 boxing0.050.130.070.110.220.650.380.700.51 youngki0.090.130.020.060.190.620.540.620.54 skating0.010.200.020.290.250.460.410.420.37 psy0.07 0.020.170.230.390.400.630.57 Dance20.030.070.100.110.140.450.300.520.50

21. Summary  Non-temporal offline tracking algorithm Actively identifying a suitable tracking order  Tracking by Bayesian model averaging Robust to intermediate failures  Hierarchical tracking Reducing empirical processing time significantly