Person Re-Identification

✍ Scribed by Shaogang Gong, Marco Cristani, Shuicheng Yan;Chen Change Loy

Publisher: Springer London
Tongue: English
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

"Demand continues to grow worldwide, from both government and commerce, for technologies capable of automatically selecting and identifying object and human behaviour. This accessible text/reference presents a comprehensive and unified treatment of visual analysis of behaviour from computational-modelling and algorithm-design perspectives. The book provides in-depth discussion on computer vision and statistical machine learning techniques, in addition to reviewing a broad range of behaviour modelling problems. A mathematical background is not required to understand the content, although readers will benefit from modest knowledge of vectors and matrices, eigenvectors and eigenvalues, linear algebra, optimisation, multivariate analysis, probability, statistics and calculus."--Publisher's website.

✦ Table of Contents

Part I. Introduction --
1. About Behaviour --
1.1. Understanding Behaviour --
1.1.1. Representation and Modelling --
1.1.2. Detectionand Classification --
1.1.3. Predictionand Association --
1.2. Opportunities --
1.2.1. Visual Surveillance --
1.2.2. Video Indexing and Search --
1.2.3. Robotics and Healthcare --
1.2.4. Interaction,Animationand Computer Games --
1.3. Challenges --
1.3.1. Complexity --
1.3.2. Uncertainty --
1.4. The Approach --
2. Behaviour in Context --
2.1. Facial Expression --
2.2. Body Gesture --
2.3. Human Action --
2.4. Human Intent --
2.5. Group Activity --
2.6. Crowd Behaviour --
2.7. Distributed Behaviour --
2.8. Holistic Awareness: Connecting the Dots --
3. Towards Modelling Behaviour --
3.1. Behaviour Representation --
3.1.1. Object-Based Representation --
3.1.2. Part-Based Representation --
3.1.3. Pixel-Based Representation --
3.1.4. Event-Based Representation --
3.2. Probabilistic Graphical Models --
3.2.1. Static Bayesian Networks --
3.2.2. Dynamic Bayesian Networks --
3.2.3. Probabilistic Topic Models --
3.3. Learning Strategies --
3.3.1. Supervised Learning --
3.3.2. Unsupervised Learning --
3.3.3. Semi-supervised Learning --
3.3.4. Weakly Supervised Learning --
3.3.5. Active Learning --
Part II. Single-Object Behaviour --
4. Understanding Facial Expression --
4.1. Classification of Images --
4.1.1. Local Binary Patterns --
4.1.2. Designing Classifiers --
4.1.3. Feature Selection by Boosting --
4.2. Manifold and Temporal Modelling --
4.2.1. Locality Preserving Projections --
4.2.2. Bayesian Temporal Models --
4.3. Discussion --
5. Modelling Gesture --
5.1. Tracking Gesture --
5.1.1. Motion Moment Trajectory --
5.1.2. 2D Colour-Based Tracking --
5.1.3. Bayesian Association --
5.1.4. 3D Model-Based Tracking --
5.2. Segmentationand Atomic Action --
5.2.1. Temporal Segmentation --
5.2.2. Atomic Actions --
5.3. Markov Processes --
5.4. Affective State Analysis --
5.4.1. Space-Time Interest Points --
5.4.2. Expressionand Gesture Correlation --
5.5. Discussion --
6. Action Recognition --
6.1. Human Silhouette --
6.2. Hidden Conditional Random Fields --
6.2.1. HCRF Potential Function --
6.2.2. Observable HCRF --
6.3. Space-Time Clouds --
6.3.1. Clouds of Space-Time Interest Points --
6.3.2. Joint Local and Global Feature Representation --
6.4. Localisationand Detection --
6.4.1. Tracking Salient Points --
6.4.2. Automated Annotation --
6.5. Discussion --
Part III. Group Behaviour --
7. Supervised Learning of Group Activity --
7.1. Contextual Events --
7.1.1. Seeding Event: Measuring Pixel-Change-History --
7.1.2. Classificationof Contextual Events --
7.2. Activity Segmentation --
7.2.1. Semantic Content Extraction --
7.2.2. Semantic Video Segmentation --
7.3. Dynamic Bayesian Networks --
7.3.1. Correlationsof Temporal Processes --
7.3.2. Behavioural Interpretation of Activities --
7.4. Discussion --
8. Unsupervised Behaviour Profiling --
8.1. Off-line Behaviour Profile Discovery --
8.1.1. Behaviour Pattern --
8.1.2. Behaviour Profilingby Data Mining --
8.1.3. Behaviour Affinity Matrix --
8.1.4. Eigendecomposition --
8.1.5. Model Order Selection --
8.1.6. Quantifying Eigenvector Relevance --
8.2. On-line Anomaly Detection --
8.2.1. A Composite Behaviour Model --
8.2.2. Run-Time Anomaly Measure --
8.2.3. On-line Likelihood Ratio Test --
8.3. On-line Incremental Behaviour Modelling --
8.3.1. Model Bootstrapping --
8.3.2. Incremental Parameter Update --
8.3.3. Model Structure Adaptation --
8.4. Discussion --
9. Hierarchical Behaviour Discovery --
9.1. Local Motion Events --
9.2. Markov Clustering Topic Model --
9.2.1. Off-line Model Learning by Gibbs Sampling --
9.2.2. On-line Video Saliency Inference --
9.3. On-line Video Screening --
9.4. Model Complexity Control --
9.5. Semi-supervised Learning of Behavioural Saliency --
9.6. Discussion --
10. Learning Behavioural Context --
10.1. Spatial Context --
10.1.1. Behaviour-Footprint --
10.1.2. Semantic Scene Decomposition --
10.2. Correlational and Temporal Context --
10.2.1. Learning Regional Context --
10.2.2. Learning Global Context --
10.3. Context-Aware Anomaly Detection --
10.4. Discussion --
11. Modelling Rare and Subtle Behaviours --
11.1. Weakly Supervised Joint Topic Model --
11.1.1. Model Structure --
11.1.2. Model Parameters --
11.2. On-line Behaviour Classification --
11.3. Localisationof Rare Behaviour --
11.4. Discussion --
12. Man in the Loop --
12.1. Active Behaviour Learning Strategy --
12.2. Local Block-Based Behaviour --
12.3. Bayesian Classification --
12.4. Query Criteria --
12.4.1. Likelihood Criterion --
12.4.2. Uncertainty Criterion --
12.5. Adaptive Query Selection --
12.6. Discussion --
Part IV. Distributed Behaviour --
13. Multi-camera Behaviour Correlation --
13.1. Multi-view Activity Representation --
13.1.1. Local Bivariate Time-Series Events --
13.1.2. Activity-Based Scene Decomposition --
13.2. Learning Pair-Wise Correlation --
13.2.1. Cross Canonical Correlation Analysis --
13.2.2. Time-Delayed Mutual Information Analysis --
13.3. Multi-camera Topology Inference --
13.4. Discussion --
14. Person Re-identification --
14.1. Re-identification by Ranking --
14.1.1. Support Vector Ranking --
14.1.2. Scalability and Complexity --
14.1.3. Ensemble Rank SVM --
14.2. Context-Aware Search --
14.3. Discussion --
15. Connecting the Dots --
15.1. Global Behaviour Segmentation --
15.2. Bayesian Behaviour Graphs --
15.2.1. A Time-Delayed Probabilistic Graphical Model --
15.2.2. Bayesian Graph Structure Learning --
15.2.3. Bayesian Graph Parameter Learning --
15.2.4. Cumulative Anomaly Score --
15.2.5. Incremental Model Structure Learning --
15.3. Global Awareness --
15.3.1. Time-Ordered Latent Dirichlet Allocation --
15.3.2. On-line Predictionand Anomaly Detection --
15.4. Discussion.

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