I. INTRODUCTION
A. VIDEO BIG DATA, CLOUD COMPUTING, AND THEIR RELATIONSHIP
II. RECENT STUDIES
III. SCOPE AND NOMENCLATURE
IV. LAMBDA CVAS: A REFERENCE ARCHITECTURE
A. VIDEO BIG DATA CURATION LAYER
1. VIDEO BIG DATA PROCESSING LAYER
2. VIDEO BIG DATA MINING LAYER
3. DISTRIBUTED DEEP LEARNING FOR IVA
4. BIG DATA ENGINES, ML LIBRARIES, AND IVA
5. COMPUTER VISION BENCHMARK DATASETS
C. KNOWLEDGE CURATION LAYER
D. WEB SERVICES LAYER
E. IVA SERVICE EXAMPLE SCENARIOS
F. EXECUTION SCENARIOS
V. IVA, CONSTITUENTS, AND PREDOMINANT TRENDS
A. CONTENT-BASED VIDEO RETRIEVAL
B. REAL-TIME INTELLIGENT VIDEO ANALYSIS AND SURVEILLANCE
1. VIDEO SEGMENTATION FOR ACTION RECOGNITION
2. BEHAVIOR ANALYSIS
3. ANOMALY DETECTION
C. VIDEO SUMMARIZATION
D. SEMANTIC-BASED VIDEO ANALYSIS
VI. STATE-OF-THE-ART CVAS
A. SCHOLARLY CVAS
B. INDUSTRIAL CVAS
VII. IVA APPLICATIONS
VIII. RESEARCH ISSUES, OPPORTUNITIES, AND FUTURE DIRECTIONS
A. IVA ON VIDEO BIG DATA
B. IVA AND HUMAN-MACHINE COORDINATION
C. ORCHESTRATION AND OPTIMIZATION OF IVA PIPELINE
D. IVA AND BIG DIMENSIONALITY
E. ONLINE LEARNING ON VIDEO BIG DATA
F. MODEL MANAGEMENT
G. PARAMETER SERVERS AND DISTRIBUTED LEARNING
H. EVALUATION ISSUES AND OPPORTUNITIES
I. IVA ALGORITHM, MODEL, AND SERVICES STATISTICS MAINTENANCE, RANKING, AND RECOMMENDATION
J. IVAAAS AND COST MODEL
K. VIDEO BIG DATA MANAGEMENT
L. PRIVACY, SECURITY AND TRUST
IX. CONCLUSION