# Deep Anomaly Detection for In-Vehicle Monitoring--An Application-Oriented Review
## 5 Challenges, Approaches and Opportunities for In-Vehicle Monitoring  
Anomaly detection in confined spaces, such as the interior of vehicles, is an interesting new application scenario for these methods. However, as the work of [PERSON] et al. [5] demonstrates, the development of solutions for this use case is still fully dependent on the availability of private datasets. A subset of a dataset provided by Bosch Car Multimedia containing videos of nine different actor pairs performing various activities in the backseat of a vehicle was used by the authors. Every video featured two actors in every frame, and the anomalies present in the subset are strictly related to violent interactions between two individuals only (e.g., slapping and punching). However, the relevance of objects was not considered in this work, whether for representing a danger to the passengers or simply as an object that was left behind by one of them. The latter is of significant importance in the suggested shared autonomous vehicle scenario. Creating new datasets or expanding existing ones appears to be an immediate need for considering new use applications for anomaly detection. The former is a complex and costly task that implies allocating resources for staging and recording the desired interactions. An additional bureaucratic effort is also required to obtain permission from the actors involved. Moreover, a post-recording labelling effort is time consuming. Hence, an attractive option relies on synthetic data that could be generated for direct use or to augment available data. The work of [PERSON] et al. [55] is referred to as an interesting approach to the translation of simulated objects to real-world datasets using a CycleGAN [56]. Similar hybrid strategies could be employed to circumvent the lack of data for in-vehicle monitoring applications. Furthermore, such strategies could pre-emptively add some artificial variety to the available video sequences. As it \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Year** & **Method** & **Availability** & **ML Framework** \\ \hline \multirow{8}{*}{Semi-supervised} & \multirow{2}{*}{2017} & S-RNN [4] & Link, a.o. & \multirow{2}{*}{TensorFlow} \\  & & ConvLSTM-AE [20] & Link, a.o. & \multirow{2}{*}{Caffe} \\  & & & 01/08/22 & \\ \cline{1-1} \cline{3-5}  & \multirow{2}{*}{2018} & \multirow{2}{*}{FFP [3]} & Link, a.o. & \multirow{2}{*}{TensorFlow} \\  & & & 29/07/22 & \\  & & ALOCC [66] & Link, a.o. & \multirow{2}{*}{TensorFlow} \\  & & & 01/08/22 & \\ \cline{1-1} \cline{3-5}  & \multirow{2}{*}{2019} & \multirow{2}{*}{Mem-AE [23]} & Link, a.o. & \multirow{2}{*}{PyTorch} \\  & & & 01/08/22 & \\  & & & 01/08/22 & \\ \cline{1-1} \cline{3-5}  & \multirow{2}{*}{2019} & \multirow{2}{*}{MNAD [36]} & Link, a.o. & \multirow{2}{*}{PyTorch} \\  & & & 27/07/22 & \\ \cline{1-1} \cline{3-5}  & \multirow{2}{*}{2020} & \multirow{2}{*}{OGNet [68]} & Link, a.o. & \multirow{2}{*}{PyTorch} \\  & & & 01/08/22 & \\ \cline{1-1} \cline{3-5}  & & & 01/08/22 & \\ \hline \multirow{8}{*}{Weakly supervised} & \multirow{2}{*}{2020} & \multirow{2}{*}{Sultani et al. [40]} & Link, a.o. & \multirow{2}{*}{Theano} \\  & & & 01/08/22 & \\ \cline{1-1} \cline{3-5}  & \multirow{2}{*}{2019} & \multirow{2}{*}{GCN [45]} & Link, a.o. & \multirow{2}{*}{PyTorch} \\  & & & 01/08/22 & \\ \cline{1-1} \cline{3-5}  & & & 01/08/22 & \\ \cline{1-1} \cline{3-5}  & \multirow{2}{*}{2020} & \multirow{2}{*}{AR-Net [42]} & Link, a.o. & \multirow{2}{*}{PyTorch} \\  & & & 01/08/22 & \\ \cline{1-1} \cline{3-5}  & & & 01/08/22 & \\ \cline{1-1} \cline{3-5}  & \multirow{2}{*}{2021} & \multirow{2}{*}{XD-Violence [59]} & Link, a.o. & \multirow{2}{*}{PyTorch} \\  & & & 29/07/22 & \\ \cline{1-1} \cline{3-5}  & & & 01/08/22 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Available source code of the surveyed methods, based on the compilation made by [PERSON] et al. [39]. Abbreviation: a.o.—accessed on date (format dd/mm/yy). was referred, the videos provided by Bosch Car Multimedia that were used by [PERSON] et al.