Yuanlu Xu Advisor: Prof. Liang Lin [email protected] 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