# Deep Anomaly Detection for In-Vehicle Monitoring--An Application-Oriented Review
## 5 Challenges, Approaches and Opportunities for In-Vehicle Monitoring
[5] contained only nine different actor pairs. The work of [PERSON] et al. [70] has linked the lack of actor independence with the underperformance of the trained models, as a bias is developed linking certain actors to certain actions, instead of learning the pattern of the action. Moreover, a larger pool of actors' characteristics, artificially increased or not, is essential to expose the model to diverse scenarios. Some additional challenges arise from the type and model of the vehicle that was used. For instance, the shape of the windows affects the background and light conditions in the captured scenes. Furthermore, the seats of the vehicle influence the range of movements of the passengers as well as their pose. The diversity of vehicles and actors are essential factors to produce a robust model. Choosing the best model for a new use case such as anomaly detection inside of a vehicle is not straightforward. The typical scenario of the reviewed publicly available datasets does not faithfully represent the new environment in which anomalies must be detected; therefore, their use does not produce an authentic benchmark of the proposed methods. Most of these sequences were captured with stationary video cameras that were recording static backgrounds. Although cameras inside vehicles are also stationary, windows on a moving vehicle produce a partially moving background on the recorded sequence. The distance between the cameras and the subjects is much smaller inside a vehicle, increasing the effect of geometric distortions on the captured information. Additionally, headlights of other vehicles, public illumination and occlusions of sunlight produce more frequent illumination perturbations in the scene than those found on datasets that focus on a pedestrian walkway, for instance. The behaviour of the available models in such scenes is uncertain, as these did not have to specifically build and test tools for such problems. However, they cannot be neglected to build a successful application for this use case. Furthermore, in the available datasets, especially the ones regarding pedestrians and crowds, the entirety of the body of the actors is visible; therefore, the models can benefit from this information to detect anomalies. However, inside confined spaces, this might not be possible. Taking into consideration the in-vehicle scenario, due to the limited available camera positions, part of the legs of the passengers are occluded, as Figure 5c demonstrates. Therefore, in such tasks, the models are limited to partial information regarding the human actors and the area in which the actions take place. None of the datasets that were analysed in Section 3 present a convenient tool for training and benchmarking a model for anomaly detection inside a vehicle or similarly confined spaces. The datasets that comprise sequences of pedestrians and crowds were mostly recorded outdoors and cover a great area when compared to the new scenario of interest. Additionally, the normal samples that these sequences present consist of people walking or simply standing, which are actions that would be considered abnormal inside a car. As far as confined spaces are concerned, the datasets that present real-world anomalies, UCF-Crime [40] and XD-Violence [59], possess some scenes that fit this context. However, the available labels do not give any information regarding the location in which the sequences take place; therefore, an additional labelling effort would be required. Moreover, they do not present coherence in terms of the placement of the camera or the type of confined spaces presented, as these videos were extracted from films or the internet. On the other hand, SVIRO-Uncertainty [63] depicts an in-vehicle scenario, despite not presenting relevant information for anomaly detection in terms of abnormal actions perpetrated by the passengers. Its potential remains solely linked to the detection of dangerous or abandoned objects, which is a subset of anomaly detection.