# Deep Anomaly Detection for In-Vehicle Monitoring--An Application-Oriented Review
## 2 Literature Review on Deep Anomaly Detection
### Fully Supervised Strategies
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Year** & **Method** & **Datasets** & **Major Contributions** \\ \hline \multirow{6}{*}{2017} & 2016 & Conv-AE [15] & A, SU, P & Estimated abnormality through the reconstruction error of the learnt AE. \\ \cline{2-5}  & \multirow{3}{*}{ConvLSTM-AE [20]} & \multirow{3}{*}{A, SU, P} & A CNN for appearance encoding and a ConvLSTM for memorising motion information of past frames were integrated with the AE. \\ \cline{3-5}  & & & & Temporally coherent Sparse Coding mapped to a Stacked-RNN, improving parameter optimisation and anomaly prediction speed. \\ \cline{2-5}  & \multirow{3}{*}{2018} & \multirow{3}{*}{FFP [3]} & \multirow{3}{*}{A, P, S} & Future frames were predicted with motion and intensity constraints and were compared with the ground truth to detect anomalies. \\ \cline{3-5}  & \multirow{3}{*}{2019} & \multirow{3}{*}{Other-Centric [33]} & \multirow{3}{*}{P, U, S} & Given an input, it used the encoded information as a query to retrieve the most relevant memory items for reconstruction. \\ \cline{3-5}  & & & & Object-centric AE to encode motion and appearance information, paired with a one-versus-rest classifier to separate normality clusters. \\ \cline{3-5}  & \multirow{3}{*}{BMAN [31]} & \multirow{3}{*}{A, P, U, S} & Introduced an inter-frame predictor to encode normal patterns, which is used to detect abnormal events in an appearance-motion joint detector. \\ \cline{3-5}  & \multirow{3}{*}{2020} & \multirow{3}{*}{VEC [27]} & \multirow{3}{*}{A, P, S} & Prediction of erased patches of incomplete video events, fully exploiting temporal information in the video. \\ \cline{3-5}  & & & & Added a memory module to record prototypical patterns of normal data in memory items, training it with compactness and separateness losses. \\ \hline \multirow{6}{*}{2021} & \multirow{3}{*}{SSMT [30]} & \multirow{3}{*}{A, P, S} & Considered the discrimination of moving objects and objects in consecutive frames; reconstruction of object-specific appearance information. \\ \cline{3-5}  & & & & Combined appearance and motion features to obtain an essential and robust representation of regularity. \\ \cline{3-5}  & \multirow{3}{*}{ROADMAP [13]} & \multirow{3}{*}{A, P, S} & Used a frame prediction network that handles objects and different scales better; introduced a noise tolerance loss to mitigate background noise. \\ \cline{3-5}  & \multirow{3}{*}{2022} & \multirow{3}{*}{BDPN [28]} & \multirow{3}{*}{A, P, S} & Introduced three constraints to regularise the prediction task from pixel-wise, cross-modal, and temporal-sequence levels. \\ \cline{3-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the most relevant surveyed methods. Abbreviations: A = CUHK Avenue [11], SU = Subway (Entry and Exit) [57], P = UCSD Pedestrian [12], U = UMN [58], S = ShanghaiTech Campus [4], UC = UCF-Crime [40], X = XD-Violence [59], UC2L = UCFCrime2 Local [54]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**AUC Score (\%)**} \\ \cline{3-5}  & & **UCF-Crime** & **CUHK Avenue** & **ShanghaiTech** \\ \hline \multirow{2}{*}{2019} & [PERSON] et al. [53] & 82.0 & - & - \\  & [PERSON] et al. [54] & 77.52 \({}^{\dagger}\) & - & - \\  & 2022 & UBnormal [55] & - & 93.2 & 83.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Frame level AUC scores of several fully supervised methods, benchmarked on CUHK Avenue [11], ShanghaiTech Campus [4] and UCF-Crime [40] datasets. Results compiled by [PERSON] et al. [39]. Legend: \({}^{\dagger}\)—The subset UCFCrime2 Local [54] was used.