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

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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.

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

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

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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)

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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

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Problem Formulation Candidacy Graph: Vertices possible matching pairs

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

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

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

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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.

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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.

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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

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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

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

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

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Result Evaluating feature and constraints effectiveness Component Analysis

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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.

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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.

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QUESTIONS?

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Cluster Sampling Generating a composite cluster

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Cluster Sampling Generating a composite cluster

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Composite Cluster Sampling state transition probability ratio posterior ratio

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Composite Cluster Sampling

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Inference Algorithm