# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 3 VBDA Architectures and Core Technologies
### VBDA Core Technologies
#### 3.2.2 AI-Driven Video Analytics  
AI models play a pivotal role in enabling spatial, temporal, and semantic understanding of complex video data. As detailed in Table 4, a wide range of deep learning architectures has been employed in VBDA from 2018 to 2025, reflecting significant evolution in both model design and application scope. Among object detection models, YOLO and R-CNN variants remain the most widely adopted. YOLO is widely adopted in latency-sensitive applications owing to its real-time inference capabilities. In contrast, R-CNN models, though more computationally demanding, are favored in scenarios requiring high detection precision, such as security surveillance and edge analytics [51]. \begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline
**Reference** & **Year** & **YOLO** & \begin{tabular}{l} **R-CNN** \\ **Family** \\ \end{tabular} & \begin{tabular}{l} **3D-CNN/** \\ **SlowFast** \\ \end{tabular} & **LSTM** & **Transformers** & **GANs** & \begin{tabular}{l} **CLIP/** \\ **VLMs** \\ \end{tabular} & \begin{tabular}{l} **SSL** \\ **Methods** \\ \end{tabular} & \
\begin{tabular}{l} **Anomaly** \\ **Models** \\ \end{tabular} &
\begin{tabular}{l} **MOT** \\ **Tackers** \\ \end{tabular} \\ \hline [PERSON] et al. [124] & 2025 & & & & & ✓ & & ✓ & & & ✓ & & ✓ & \\ [PERSON] et al. [125] & 2025 & ✓ & & & ✓ & ✓ & & & & & \\ [PERSON] et al. [126] & 2024 & ✓ & ✓ & & & ✓ & ✓ & & & & \\ [PERSON] et al. [33] & 2024 & & & & ✓ & ✓ & ✓ & & & & & ✓ & ✓ \\ [PERSON] et al. [127] & 2024 & ✓ & & & ✓ & & & & ✓ & ✓ & & & \\ [PERSON] et al. [31] & 2024 & & & & ✓ & ✓ & ✓ & ✓ & ✓ & & & & \\ [PERSON] et al. [8] & 2024 & & & & ✓ & & & & ✓ & & & & \\ [PERSON] et al. [51] & 2024 & ✓ & ✓ & & ✓ & & & & & & & & ✓ \\ [PERSON] et al. [128] & 2024 & ✓ & & & ✓ & & & & & & & & ✓ \\ [PERSON] et al. [54] & 2024 & ✓ & ✓ & & ✓ & & & & & & & ✓ & \\ [PERSON] et al. [129] & 2023 & & ✓ & & & ✓ & & & & & & & \\ [PERSON] et al. [130] & 2023 & & & & & ✓ & ✓ & & & & & & \\ [PERSON] et al. [131] & 2023 & ✓ & ✓ & & & ✓ & & & ✓ & ✓ & & & \\ [PERSON] et al. [5] & 2023 & ✓ & ✓ & & ✓ & & & & & & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 4: AI and deep learning for video analytics in VBDA. \begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline
**Reference** & **Year** &
\begin{tabular}{l} **Hadoop** \\ **(Map Reduce)** \\ \end{tabular} & **Kafka** & **Spark** & **Flink** & **NiFi** & **Hive** & **Impala** & **Pig** & **Storm** \\ \hline [PERSON] et al. [100] & 2019 & ✓ & ✓ & ✓ & ✓ & ✓ & & & & & \\ [PERSON] et al. [30] & 2019 & & ✓ & ✓ & ✓ & & & & & & \\ [PERSON] et al. [121] & 2019 & & & ✓ & ✓ & & & & & & & ✓ \\ [PERSON] et al. [122] & 2018 & & ✓ & ✓ & & & & & & & & \\ [PERSON] et al. [123] & 2018 & ✓ & & ✓ & & & & ✓ & ✓ & & & \\ \hline
**Total** & & **6** & **17** & **16** & **4** & **3** & **6** & **3** & **1** & **3** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._For temporal modeling, LSTM-based architectures have gradually been supplanted by Transformer models, which demonstrate superior capabilities in capturing long-range dependencies, multi-modal learning, and contextual scene understanding [130]. These models are now commonly used for video summarization, action recognition, and event detection across heterogeneous VBDA workloads. In parallel, self-supervised learning (SSL) techniques such as contrastive learning are gaining prominence for their ability to reduce reliance on labeled data [132]. SSL enables systems to extract meaningful features from raw video streams, making it highly relevant for tasks such as anomaly detection, motion prediction, and behavior recognition, especially under privacy constraints where the annotation is costly or infeasible. Generative models, including GANs and diffusion-based architectures, contribute to simulation, rare event reconstruction, and data augmentation.