# Deep Anomaly Detection for In-Vehicle Monitoring--An Application-Oriented Review
## 2 Literature Review on Deep Anomaly Detection
### Fully Supervised Strategies  
The exploration of fully supervised anomaly detection remains limited by the high cost of collecting large-scale data or generating sufficiently broad artificial dataset solutions. However, semi-supervised and weakly supervised methods, despite their recent improvements, have not fully addressed their limitations, such as background bias. [PERSON] et al. [53] conducted a series of experiments to validate the existence of a background-bias phenomenon, i.e., the tendency for deep neural networks to learn the background information rather than the anomaly pattern. To tackle this, a portion of UCF-Crime [40] was re-annotated with temporal and spatial labels, the latter being represented by bounding boxes. This new information was used to feed an end-to-end framework with a designed region loss, explicitly guiding the model to focus on the anomalous region. A similar approach was implemented by [PERSON] et al. [54], focusing on spatiotemporal tubes instead of the entirety of video segments containing full frames. UCFCrime2 Local, an enriched subsection of 100 burglary and assault sequences from UCF-Crime [40], was presented as a separate dataset for anomaly detection with bounding box supervision in its train and test set. Furthermore, the proposed trainable model for anomaly detection was designed to be capable of dealing with different abnormal locations in the same video segment, and the obtained results demonstrate that locality is robust to different kinds of errors in the tube extraction phase at test time. Moreover, the proposed model was able to provide spatiotemporal proposals for unseen surveillance videos leveraging only video-level labels, enlarging the anomaly dataset without additional human labelling. [PERSON] et al. [55] presented UBnormal, a synthetic dataset generated in Cinema4D that introduces abnormal events annotated at the pixel level in the training set, enabling the use of fully supervised anomaly detection methods for the first time. Nevertheless, simulated scenes belong to a different data distribution than natural scenes. Hence, the behaviour of fully supervised models trained on this dataset might be unclear when it is applied to real-world scenes. To bridge the gap between synthetic and real-world datasets, [PERSON] et al. [55] proposed the translation of simulated objects from UBnormal to datasets such as Avenue [11] and ShanghaiTech [4] using a CycleGAN [56]. The results presented in the paper demonstrate that this hybrid solution of data augmentation can \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Feature Extractor**} & \multicolumn{2}{c}{**AUC Score (\%)**} \\ \cline{3-5}  & & & **ShanghaiTech** & **UCF-Crime** \\ \hline
2020 & AR-Net [42] & I3D-RGB \& 13D Flow & 91.24 & - \\ \hline \multirow{4}{*}{2021} & MIST [49] & I3D-RGB & 94.83 & 82.30 \\  & RTFM [46] & I3D-RGB & 97.21 & 84.30 \\ \cline{1-1}  & CRFD [50] & I3D-RGB & 97.48 & 84.89 \\ \cline{1-1}  & BD-LSTM [51] & ResNet-50 & - & 85.53 \\ \hline \multirow{3}{*}{2022} & \multirow{3}{*}{MSL [47]} & C3D-RGB & 94.81 & 82.85 \\ \cline{1-1}  & & I3D-RGB & 96.08 & 85.30 \\ \cline{1-1}  & & VideoSwin-RGB & 97.32 & 85.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._enhance the performance of state-of-the-art anomaly detection models. The performance of the aforementioned methods can be evaluated through the benchmark results exhibited in Table 3. Furthermore, a summary of the most relevant surveyed methods with different supervision strategies is provided in Table 4. The datasets referenced in both tables are fully explored in Section 3.