# Deep Anomaly Detection for In-Vehicle Monitoring--An Application-Oriented Review
## 6 Conclusions  
In this article, various deep learning methods for anomaly detection in videos were discussed. Studying the defining characteristics of state-of-the-art methods is important not only to gain a better understanding of the general problem of anomaly detection but also to understand how the offered solutions could fit into the new scenario of interest: in-vehicle monitoring. The major contributions of the analysed works are briefly summarised in Table 4. Additionally, Table 6 provides a compilation of the available source code; the code present in these repositories comprises an interesting starting point for replicating and improving these models for new applications. The analysis of state-of-the-art techniques provided a deeper understanding of the background of these models and its influence on their current limitations regarding in-vehicle monitoring. The focus on crowded scenes and outdoor spaces led to a failure to consider problems associated with the nature of this new scenario. For instance, the surveillance of Shared Autonomous Vehicles must consist of a much closer recording of the subjects, which raises questions about the importance of actor independence and the effect of geometric distortions on the captured information caused by the lens of the camera. Moreover, these models assume a mostly static background, although the movement of the car and the presence of windows result in moving backgrounds. Additionally, frequent illumination changes (e.g., a cloud covering the sun) result in a more intense impact on the visual information in such scenarios. The main limitation of the implementation of anomaly detection solutions to in-vehicle monitoring is the lack of data samples explicitly dedicated to the detection of abnormal behaviours inside a vehicle or similarly confined spaces. Hence, there are currently no public datasets that could be directly used as a tool for training and benchmarking such models. The development of solutions for this use case is still fully dependent on the availability of private datasets. Although newer datasets have been adapted to benchmark models created for anomaly detection tasks, their original focus was related to action recognition tasks. The reviewed synthetic datasets presented high-quality images with an interesting amount of annotations but do not comprise a compatible set of data instances for this task. Although this is a severe challenge, it also provides a great opportunity to study techniques for data augmentation and generation. In this paper, several approaches were proposed to be implemented in future works. They can be summarised in a two-stage process. Firstly, available datasets, mainly the ones covering diverse real-world anomalies, must be extensively studied to find instances representative of the real-world settings that the systems will face, providing an initial reference. Secondly, the expansion and adaptation of similar instances should be contemplated. This could be achieved through the translation of simulated objects or actors to tackle the lack of available sequences but also their reduced diversity of actors, actions and significant illumination changes. This approach could also reduce the labelling effort of new captures. This review initiates an important discussion on application-oriented issues related to deep anomaly detection for in-vehicle monitoring, which is a field that presents a high potential for exploration in future works. Other surveys and reviews have disregarded this scenario and its specificities, despite its relevance, as shown by the funded projects, such as Prevent PCP, that aim to take advantage of innovative solutions for applying anomaly detection to this scenario. Moreover, in-vehicle monitoring increases the interest in the optimisation effort of anomaly detection models for embedded systems, as its implementation requires the capability of running locally in resource-limited hardware. **Author Contributions:** Conceptualisation by F.C., P.C.