# Deep Anomaly Detection for In-Vehicle Monitoring--An Application-Oriented Review
## 2 Literature Review on Deep Anomaly Detection
### Weakly Supervised Strategies
[45] reformulated the task as a binary classification under a noisy label problem and suggested the use of a Graph Convolution Neural (GCN) network to correct low-confidence anomaly scores, replacing them with high-confidence ones, i.e., clear the label noise. In the GCN, two characteristics of a video were considered to correct the label noise: temporal consistency, and feature similarity. Feature similarity assures that the abnormal snippets share similar characteristics; temporal consistency guarantees that abnormal instances appear in temporal proximity of each other. Even though this work achieved better accuracy in the identification of anomalies when compared to MIL-based approaches, training both a GCN and MIL is computationally expensive and may cause unstable performance due to unconstrained latent space. [PERSON] et al. [47] proposed another approach capable of addressing the shortcomings of MIL-based methods. The authors of this paper used a Multi-Sequence Learning (MSL) method, opting for choosing the sequence with the highest sum of anomaly scores instead of the instance with the highest score, reducing the probability of incorrect selection. This method encoded the extracted snippet features via a multi-layer Convolutional Transformer Encoder, using VideoSwin [48] as the backbone to extract them. The benchmark results shown in Table 2 indicate that VideoSwin performed consistently better than traditional feature extractors, such as C3D-RGB and I3D-RGB. The goal of video anomaly detection must be the prediction of fine-grained anomaly scores. Although some works focused on detecting anomalies in an offline or coarse-grained manner, these do not allow real-time monitoring, reducing the interest in applying them to real-world scenarios. Instead of using video-level labels as pseudo-labels, [PERSON] et al. [49] suggested the use of the learnt pseudo-labels to optimise the feature encoder. This method was capable of working in an online fine-grained manner. In a similar fashion to [PERSON] et al. [47], a two-stage self-training strategy was used. \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
2018 & [PERSON] et al. [40] & C3D-RGB & 86.3 \({}^{\ddagger}\) & 75.41 \\ \hline \multirow{4}{*}{2019} & IBL [41] & C3D-RGB & - & 78.66 \\  & C3D-RGB & 76.44 & 81.08 \\ \cline{1-1}  & GCN-Anomaly [45] & TSN-Flow & 84.13 & 78.08 \\ \cline{1-1}  & & TSN-RGB & 84.44 & 82.12 \\ \cline{1-1}  & Motion-Aware [44] & PWC-Flow & - & 79.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Frame level AUC scores of several weakly supervised methods, benchmarked on ShangaiTech Campus [4] and UCF-Crime [40] datasets. Results compiled by [PERSON] et al. [39] Legend: \({}^{\ddagger}\). Figure 2: Architecture of a typical MIL-based method, such as the one proposed by [PERSON] et al. [40]. [PERSON] et al. [51] developed an approach that aims to reduce the processing time required for deep anomaly detection. For this purpose, the features extracted from the sequence of frames were fed to a Bi-directional Long Short-term Memory (BD-LSTM) model, which differs from a regular LSTM by depending not only on the previous frames but also on the upcoming ones. This work was followed by [PERSON] et al. [52], decomposing the anomaly detection process into two stages. Firstly, a Raspberry Pi, a resource-limited device, runs a lightweight version of the network. If an anomaly is detected, the captured data are transmitted to a cloud centre for a detailed analysis. The performance of weakly supervised methods can be evaluated through the benchmark results illustrated in Table 2. The datasets referenced in the table are fully explored in Section 3.