# Deep Anomaly Detection for In-Vehicle Monitoring--An Application-Oriented Review
## 2 Literature Review on Deep Anomaly Detection
### Weakly Supervised Strategies  
Weakly supervised video anomaly detection strategies are amongst the best-performing approaches for this task. At the expense of an additional low-intensity annotation effort, a better anomaly classification accuracy can be achieved. Essentially, weakly supervised approaches can be subdivided into two classes, encoder-agnostic and encoder-based methods. Encoder-agnostic methods [40; 41; 42] leveraged task agnostic features of videos extracted from a vanilla feature encoder (e.g., J3D [43]) to estimate the anomaly scores of each frame. In these methods, only the classifier was trained. On the other hand, encoder-based methods [44; 45] trained both the feature encoder and classifier simultaneously. Weakly supervised strategies are considered to be a feasible method due to their competitive performance. [PERSON] et al. [40] introduced the use of video-level labels in the tasks of anomaly detection in videos by presenting UCF-Crime, which is a large-scale video dataset for training and testing weakly supervised anomaly detection approaches. Along with this strategy, [PERSON] et al. [40] proposed a deep Multiple Instance Learning (MIL) ranking framework to detect anomalies, as illustrated in Figure 2. In essence, MIL takes a video as a bag and clips the video as separate instances. The bag generated from an abnormal instance is called a positive bag, and it must contain at least one abnormal snippet. The negative bag, generated from normal videos, contains no abnormal snippets. The instance-level anomaly scores are learnt through the bag-level labels. Several papers followed the MIL framework, suggesting improvements to the method. The inner-bag score gap regularisation was introduced by [PERSON] et al. [41] to increase the gap between the lowest and highest scores in a positive bag and reduce it in a negative one. [PERSON] et al. [42] proposed a dynamic MIL-loss and centre-guided regularisation; the former enlarged the interclass dispersion, and the latter reduced the intraclass distance of normal snippets. Additionally, [PERSON] et al. [44], in an encoder-based approach, suggested an attention-based MIL model capable of encoding motion-aware features by using an autoencoder based on optical flow. To unify the representation learning and anomaly score learning, a temporal feature ranking loss was presented by [PERSON] et al. [46]. This approached proved to be capable of achieving a better separation of normal and abnormal features, improving the exploration of the weak-labelling strategy in comparison to the previous MIL methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**AUC Score (\%)**} \\ \cline{3-4}  & & **CUHK Avenue** & **ShanghaiTech** \\ \hline
2016 & Conv-AE [15] & 70.2 & 60.85 \({}^{\dagger}\) \\ \hline
2017 & ConvLSTM-AE [20] & 77.0 & - \\  & S-RNN [4] & 81.7 & 68.0 \\ \hline
2018 & FFP [3] & 85.1 & 72.8 \\ \hline
2019 & Mem-AE [23] & 83.3 & 71.2 \\  & Object-Centric [33] & 90.4 & 84.9 \\ \hline
2020 & MNAD [36] & 88.5 & 70.5 \\  & VEC [27] & 90.2 & 74.8 \\ \hline
2021 & SSMT [30] & 86.9 & 83.5 \\  & ROADMAP [13] & 88.3 & 76.6 \\  & AMMC [37] & 86.6 & 73.7 \\ \hline
2022 & BDPN [28] & 90.3 & 78.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Frame-level AUC scores of several semi-supervised methods, benchmarked on CUHK Avenue [11] and ShanghaiTech Campus [4] datasets. Results compiled by [PERSON] et al. [39] Legend: \({}^{\dagger}\)—Computed by [PERSON] et al. [4]. [PERSON] et al. [45] denoted that the methods that used Multiple Instance Learning suffered from error propagation throughout the training. If the model incorrectly predicted anomalous instances in the positive bag, the error would affect subsequent instance selection. To tackle this problem, [PERSON] et al.