1. Particle Dynamics and Multi- Channel Feature Dictionaries for Robust Visual Tracking Srikrishna Karanam, Yang Li, Rich Radke Dept. of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy NY

2. Compressive sensing tracking 2 Feature dictionary

3. Compressive sensing tracking 3 Curren t state Hypothese s

4. Compressive sensing tracking 4 Hypothesis Testing Sparse x, e

5. Contributions APPEARAN CE MODEL Multi-channel feature dictionaries Image intensity Image gradient magnitude Histograms of Oriented Gradients HYPOTHESI S GENERATIO N Particle filter Adaptive variance Gaussian State Transition Model 5

6. Appearance model 6 Intensity Normalized gradient magnitude Histograms of Oriented Gradients Norm. Gradien t HOG ∑ Intensit y ∑ ∑

7. Hypothesis generation – Transition model 7 Past state vecto rs Z. Hong et al., Tracking via robust multi-task multi-view joint sparse representation, ICCV 2013.

8. Hypothesis generation – Transition model 8 M. Ayazoglu et al., Dynamic subspace-based coordinated multicamera tracking, ICCV 2011. (1 ) (2 ) (3 ) (4 ) (5 ) (6 )

9. Hypothesis generation – Particle filtering 9 Related approaches – (400-600, fixed) Dynamic model + adaptive candidate filtering D. Fox, KLD-Sampling: Adaptive Particle Filters, NIPS 2001.

10. Hypothesis testing 10 FISTA Analytic

11. Data Publicly available standard test sequences 11 Focal Length Y. Wu et al., Online object tracking: a benchmark, CVPR 2013.

12. Evaluation metrics Success plot Robustness tests Temporal robustness test Spatial Robustness test 12 Principal PointFocal Length Overlap precision vs. Overlap threshold

13. Experimental Results – Overall Success Plot Ideally, close to 1 13 Principal PointFocal Length

14. Experimental Results – Robustness tests Temporal robustness evaluation Spatial robustness evaluation 14 Principal PointFocal Length

15. Experimental Results Validating key components. Choice of features. Choice of transition model. Adaptive candidate filtering 15 Principal PointFocal Length Distortion Coefficient

16. Speed 16 MethodSpeed (fps) Templat e size Average distance precisio n Average AUC Ours*2.564 x 640.920.69 L1*8.212 x 150.470.36 MTT*0.432 x 320.600.42 ONDL*0.532 x 320.790.59 SCM*0.0532 x 320.720.59 ASLA*0.732 x 320.730.59 LSH7-0.700.57 LOT0.2-0.530.31 SPT0.1-0.490.29 MIL8.5-0.560.45 IVT6.5-0.610.46 * - based on sparse visual representation.

17. This material is based upon work supported by the U.S. Department of Homeland Security, Science and Technology Directorate, Office of University Programs, under Award 2013-ST-061-ED0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security. Conclusions Multi-Channel features Particle dynamical information Adaptive filtering Thank you! Questions?