# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 2 Literature Overview in VBDA
### Problem Taxonomy and Applications of Video Processing in VBDA
#### 2.4.6 Video Summarization and Captioning  
Video summarization transforms extended footage into succinct representations, thereby facilitating efficient review, archival, and content personalization. This is particularly valuable in applications like surveillance review, broadcast editing, and education. Recent approaches go beyond keyframe extraction to include generative models such as VST [48], ViViT, and BLIP-2 for producing text summaries or abstract visual synopses. Commonly used benchmarks include SumMe [86] and TVSum [87]. A structured summary of key problem domains in VBDA is presented in Table 1, outlining core technologies, datasets, and application contexts. While these tasks have enabled practical deployments in domains such as transportation and healthcare, transitioning from research prototypes to scalable production systems remains hindered by unresolved technical challenges. A primary barrier is scalability and system heterogeneity. Real-world VBDA must handle continuous, high-resolution video from diverse sources such as CCTV cameras, drones, and IoT devices. This introduces substantial demands on bandwidth, storage, and computational infrastructure. Coordinating processing across decentralized layers spanning edge and cloud adds complexity in terms of orchestration, resource allocation, and vendor interoperability. Latency and responsiveness represent additional bottlenecks. Despite improvements in deep learning accuracy, many state-of-the-art models remain too resource-intensive for real-time inference on edge-constrained hardware. Meeting low-latency, high-throughput requirements calls for end-to-end optimization across both model design and system architecture. Privacy and compliance are equally critical, especially in domains involving sensitive video data. Regulatory frameworks such as GDPR and HIPAA require secure governance practices, including FL, encrypted computation, and verifiable data workflows to prevent misuse and ensure data sovereignty. Finally, limited labeled data remains a critical bottleneck in advancing robust VBDA systems. Most current approaches rely heavily on large-scale supervised learning, yet collecting and annotating video data is both time-consuming and costly. To address this challenge, self-supervised and semi-supervised learning techniques have been increasingly adopted as alternatives to fully supervised approaches. Nevertheless, these models \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Problem Class** & **Representative Technologies** & **Benchmark Datasets** & **Applications** & **References** \\ \hline
**Object Detection and Tracking** & YOLO, Faster R-CNN, SSD, DeepSORT, FairMOT; PointNet, VoxelNet & COCO, KITTI, VisDrone, AlCity & Surveillance, Traffic Monitoring, Autonomous Driving,Sports & [88; 89; 90; 91] \\
**Activity and Behavior** & 30 CNNs, LSTM, SlowFast, Transformers; OpenPose, HRNet, Multimodal Fusion & UCT-101, HMDB-51, NTU RGB+D, Kinetics & Healthcare, Human-Robot Interaction, Smart Spaces, Sports & [70; 92; 93; 94] \\
**Anomaly Detection** & Autoencoders, Isolation Forest, GANs, CNNs, Transformers & UCSD, Avenue, ShanghaiTech & Intrusion Detection, Industrial Safety, Emergency Response & [5; 74; 95] \\
**Crowd Analysis** & CNN Density Maps, Optical Flow, YOLO, GANs, Transformers & UCF-QNRF, ShanghaiTech, Mall & Event Safety, Urban Planning, Transit Optimization & [77; 96; 79] \\
**Content-Based Retrieval and Understanding** & VideoBERT, CLIF, FAISS, Pinecome, ElasticSearch & YouTube-8M, ActivityNet, MSR-VTT & Surveillance Forensics, Media Search, Smart Recommender & [82; 93; 97] \\
**Video Summarization and Captioning** & VST, ViViT, TimeSformer; BLIP-2, GPT-4V; Diffusion Models & TVSum, SumMe, Kinetics, Show and Tell & Surveillance Triage, Media Editing, Accessibility & [92; 98; 99] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Problem taxonomy, core technologies, and applications in VBDA.