# Deep Anomaly Detection for In-Vehicle Monitoring--An Application-Oriented Review
## 3 Publicly Available Datasets
### Real-World Datasets  
#### 3.1.1 Pedestrians and Crowds  
The UMN dataset [58] was one of the first datasets used for benchmarking tasks of video anomaly detection. However, it had several limitations that led to a loss of relevance in recent benchmarks, mainly the existence of a single anomaly type. UMN is composed of 11 short clips of three indoor or outdoor scenes in which people suddenly start running away. There is no clear split between training and testing frames, and anomalies are only temporally labelled. Subway [57] has been declining in popularity for similar reasons. This indoor dataset is divided into two separate instances: Entrance and Exit. Only two long videos are provided, and there is uncertainty about which frames should be labelled as anomalous and used for the training and testing processes. The number of types of anomalies is reduced and include people jumping over the turnstiles or walking in the wrong direction. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Year** & **Method** & **Datasets** & **Major Contributions** \\ \hline \multirow{6}{*}{2019} & \multirow{2}{*}{2018} & \multirow{2}{*}{[PERSON] et al. [40]} & \multirow{2}{*}{UC} & Learnt anomaly through an MIL framework by learning a ranking model that predicts high anomaly scores for anomalous video segments. \\ \cline{3-5}  & & & & Used an inner bag loss for MIL to increase the gap between the lowest and highest scores in a positive bag and reduce it in a negative one. \\ \cline{3-5}  & & & & A GCN was used to clean label noise, to directly apply fully supervised action classifiers to weakly supervised anomaly detection. \\ \cline{3-5}  & & \multirow{2}{*}{Motion-Aware [44]} & \multirow{2}{*}{UC} & \multirow{2}{*}{Added temporal context to the MIL ranking model by using an attention block; the attention weights helped to identify anomalies better.} \\ \cline{3-5}  & & & & A dynamic MIL loss enlarged the interclass dispersion; a centre loss reduced the intraclass distance of normal snippets. \\ \hline \multirow{6}{*}{2021} & \multirow{2}{*}{2020} & \multirow{2}{*}{MIST [49]} & \multirow{2}{*}{S, UC} & Implemented a pseudo-label generator and an attention-boosted feature encoder to focus on anomalous regions. \\ \cline{3-5}  & & & & A feature magnitude learning function was trained to recognise positive instances; self-attention mechanisms captured temporal dependencies. \\ \cline{3-5}  & & & & Captured local-range temporal dependencies; enhanced features to the category space and further expanded the temporal modeling range. \\ \cline{3-5}  & & & & BD-LSTM [51] & UC, UC2L \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \hline \multirow{6}{*}{2022} & \multirow{2}{*}{MSL [47]} & \multirow{2}{*}{S, UC, X} & Transformer-based MSL network to learn both video-level anomaly probability and snippet-level anomaly scores. \\ \cline{3-5}  & & & & & Implemented a region loss to explicitly drive the network to learn the anomalous region; a meta learning module prevented severe overfitting. \\ \cline{3-5}  & & & & Considered spatiotemporal tubes instead of whole-frame video segments; existing videos were enriched with spatial and temporal annotations to allow bounding box supervision in both its train and test set. \\ \cline{1-1} \cline{2-5}  & & & & & Proposed the translation of simulated objects from its dataset to others using a CycleGAN, increasing performance. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._The most widely used video anomaly detection dataset is the UCSD pedestrian dataset [12]. It was captured by a stationary video camera, focusing on two pedestrian walkways. This dataset contains two separate subsets: Ped1 and Ped2. The former is composed of 34 training videos and 36 testing videos, whilst the latter consists of 16 training video clips and 12 testing ones. The abnormal behaviours are connected to the presence of vehicles such as cars and bikers, as illustrated by Figure 3a,b.