# Deep Anomaly Detection for In-Vehicle Monitoring--An Application-Oriented Review
## 1 Introduction  
The proliferation of cameras and the growing availability of cheap storage, coupled with the increasing demand for security, have fuelled the development of ever more complex video surveillance systems. While the task of capturing the images of possible transgressions has been greatly facilitated, appointing human controllers (e.g., a security guard) with the duty of analysing repetitive and monotonous images, as well as a multiple-camera perspective, represents a critical flaw. Such an exhausting human effort makes it very difficult for the controller to remain vigilant at all times, which might lead to abnormal events going unnoticed. In safety-critical domains, such as the detection of suspect packages in airports or train stations, neglected occurrences may have dangerous results. The identification of unexpected events, behaviours or objects can be recast as an anomaly detection problem [1, 2]. The application of deep anomaly detection methods is essential to develop new surveillance and monitoring systems that do not rely solely on human supervision, reducing the risk of the aforementioned drawbacks. Despite years of research and development, the detection of anomalies in videos remains challenging, and it differs from the traditional classification problem in two large aspects. Firstly, new kinds of anomalies are constantly arising, making it virtually impossible to list all of them. Secondly, the task of collecting sufficient negative samples is costly due to their rarity. A popular method for deep anomaly detection consists of using videos of normal events as training data. A test set is then used to detect the abnormal events which would not conform to themodel that was trained [3; 4]. This approach aims to circumvent the difficulty of gathering sufficient samples representing anomalies. Most of the frames that these systems analyse represent normal scenarios; hence, the gathered data are representative of this low ratio of abnormal snippets, and fully supervised approaches are not viable. Deep learning approaches to the detection of visual data instances that markedly digress from regular sequences have been mostly focusing on outdoor video-surveillance scenarios, mainly regarding abnormal behaviour and suspicious or abandoned object detection. A pertinent research opportunity for anomaly detection that has been overlooked in the accessible literature is posed by in-vehicle monitoring specially using solely visual data. With the increasing relevance of public transport in urban mobility, several funded projects aiming to develop autonomous surveillance systems in this area have appeared. For instance, Prevent PCP involves some of the biggest transport operators in Europe, which consider that a concerted effort is required to develop systems capable of detecting abnormal behaviours that put passengers' safety at risk. In the initial stages of this project, the lack of task-oriented datasets has been noted; an effort to acquire and label the required footage to build a dedicated dataset has been programmed. However, in-vehicle monitoring is not limited to public transport, in which large crowds must be monitored; the advent of Shared Autonomous Vehicles [5], which do not have a driver responsible for maintaining the well-being of passengers, must be accompanied by competent and reliable autonomous in-vehicle surveillance systems. The development of robust solutions for in-vehicle monitoring is not straightforward, as the conditions in which it must operate are very challenging and different from those that the methods that cover outdoor video surveillance face. Nonetheless, a recapitulation of relevant state-of-the-art techniques and available resources is essential to investigate their applicability and to achieve a deeper understanding of the potential issues raised by the new scenario that these methods did not contemplate. Furthermore, the same principle must be applied to the analysis of available datasets to train and benchmark such models.