1. Yuanlu Xu Advisor: Prof. Liang Lin merayxu@gmail.com Person Re-identification by Matching Compositional Template with Cluster Sampling

2. Problem Identifying The Same Person Under Different Cameras Person Re-identification Basic Assumption: 1.Face is unreliable due to view, low resolution and noises. 2.People's clothes should remain consistent.

3. Large Intra-class Variations Difficulty Pose/View Variation Illumination Change Occlusion

4. Problem Query Person S vs. SM vs. S Scene Search Multiple Setting

5. Representation 1.Body into 6 parts, limbs further into 2 symmetric parts. 2.Leaf nodes contain multiple instances. 3.Contextual relations between parts: kinematics symmetry. Multiple-Instance Compositional Template (MICT)

6. Problem Formulation Given the template, the problem is formulated as Selecting an instance for each part. Finding the matched part in target. … Matching-based Formulation

7. Problem Formulation Candidacy Graph: Vertices – possible matching pairs

8. Solving the problem: Labeling vertices in the graph (selecting matching pairs) NP hard – incorporating graph edges Problem Formulation

9. Compatible Edges: Encouraging matching pairs to activate together in matching Defined by contextual constraints Problem Formulation

10. Competitive Edges: Depressing conflicting matching pairs being selected at the same time Defined by matching constraints

11. Inference Using Cluster Sampling [1] for inference: 1.Sampling edges in candidacy graph to generate clusters. 2.Randomly selecting/deselecting the clusters. 3.Decide whether to accept the new state. [1] J. Porway et al., “C4: Exploring multiple solutions in graphical models by cluster sampling”, TPAMI 2011.

12. Dataset VIPeR Dataset: 1. Classic ReID dataset 2. Well-segmented people, limited pose/view 3. Heavy illumination changes, lack occlusion D. Gray et al., "Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features”, ECCV 2008.

13. Dataset EPFL Dataset: 1. Cross-camera tracking dataset 2. Few people, shot scene provided, various pose/view 3. Little illumination changes, limited occlusions F. Fleuret et al., "Multiple Object Tracking using K- Shortest Paths Optimization”, TPAMI 2011. Query InstanceVideo ShotTarget Individual

14. Dataset CAMPUS-Human Dataset: 1. Camera and annotate by us 2. Many people, shot scene provided, various pose/view 3. Limited illumination changes, heavy occlusions Query InstanceVideo ShotTarget People

15. Result Setting 1: Re-identify people in segmented images, i.e. targets already localized.

16. Result Setting 2: Re-identify people from scene shots without provided segmentations.

17. Result Evaluating feature and constraints effectiveness Component Analysis

18. Conclusion 1.A solution for a new surveillance problem. 2.A person-based model, a graph-matching-based formulation, a more complete database for evaluation. 3.Exploring robust and flexible person models [1], efficient search method [2] in future. [1] J. B. Rothrock et al., “Integrating Grammar and Segmentation for Human Pose Estimation”, CVPR 2013. [2] J. Uijlings et al., “Selective Search for Object Recognition”, IJCV 2013.

19. Published Papers 1.Yuanlu Xu, Liang Lin, Wei-Shi Zheng, Xiaobai Liu. “Human Re-identification by Matching Compositional Template with Cluster Sampling”. ICCV 2013. 2.Liang Lin, Yuanlu Xu, Xiaodan Liang, Jian-Huang Lai. “Complex Background Subtraction by Pursuing Dynamic Spatio-temporal Manifolds”. IEEE TIP 2014, under revision. 3.Yuanlu Xu, Bingpeng Ma, Rui Huang, Liang Lin. “Person Search in a Scene by Jointly Modeling People Commonness and Person Uniqueness”. ACMMM 2014, submitted.

20. QUESTIONS?

21. Cluster Sampling Generating a composite cluster

22. Cluster Sampling Generating a composite cluster

23. Composite Cluster Sampling state transition probability ratio posterior ratio

24. Composite Cluster Sampling

25. Inference Algorithm