# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 2 Literature Overview in VBDA
### Technologies and AI Enhancements in VBDA
#### 2.3.2 AI Capabilities and Integration  
Recent advancements in artificial intelligence have substantially enhanced the functional scope of VBDA systems, particularly in tasks such as object detection, activity recognition, summarization, and semantic interpretation of large-scale video data.  
AI-driven video analytics commonly employ deep learning models, including convolutional neural networks (CNNs) [47] and Transformer-based architectures [48]. State-of-the-art detectors such as YOLO, Vision Transformers (ViT), and EfficientDet have demonstrated high accuracy in dynamic and complex video environments [49]. While these models are well-suited for high-level tasks such as face recognition and behavior detection, their deployment in real-time settings is often limited by substantial computational demands [5].  
Edge AI offers a promising solution for reducing latency by shifting inference closer to the data source. By processing frames locally, edge devices can minimize reliance on cloud infrastructure and reduce bandwidth usage [50]. Lightweight architectures such as TinyML enable on-device analysis for continuous video streams. Nonetheless, the performance of edge-based inference is frequently limited by hardware constraints, particularly in terms of memory capacity and computational throughput [51]. Moreover, achieving synchronized inference between edge and cloud environments remains a persistent challenge [52].  
FL further supports privacy-preserving analytics by enabling decentralized model training across distributed nodes without transferring raw video data [53]. This paradigm is particularly useful in privacy-sensitive domains such as healthcare and surveillance. Nonetheless, FL introduces communication overhead, and synchronization becomes increasingly complex in the presence of non-IID data and unreliable network conditions [54].  
In recent years, generative models such as GANs and diffusion-based architectures have gained traction in tasks like video augmentation, anomaly detection, and spatiotemporal prediction [55]. These models enhance semantic understanding and dataset diversity but are often computationally intensive, limiting their applicability in latency-sensitive scenarios [56].  
In parallel, blockchain-based approaches have been explored to support secure provenance tracking, access control, and authentication in decentralized video systems. Smart contracts enable immutable logging of video events and transparent authorization mechanisms [57]. However, the high computational cost and latency associated with current blockchain protocols hinder their deployment in real-time video pipelines [33].  
Despite notable progress, VBDA systems still face substantial barriers related to latency, model complexity, and infrastructure coordination. Edge-cloud coordination lacks robust synchronization, and privacy-preserving methods such as FL increase communication load. In addition, adaptive video indexing and semantic retrieval continue to be computationally demanding, especially in large-scale and dynamic environments.