# Deep Anomaly Detection for In-Vehicle Monitoring--An Application-Oriented Review
## 4 Projects and Resources  
### Projects  
Recently, several international projects intending to bridge the gap between research and the market for the next generation of security solutions have been launched. These projects do not focus solely on video surveillance of public spaces, one example being PREVENT PCP, which was launched to procure innovative and advanced systems to support security in public transport. This project involves some of the biggest transport operators in Europe, which consider that a concerted effort is required to develop solutions  
Figure 5: Comparison between normal and abnormal frames extracted from synthetic datasets. (**a**) Frame from normal sequence in Ubnormal [55], showing pedestrians walking down a street. (**b**) Frame from abnormal sequence in Ubnormal [55]. Anomalous region emphasised with red contour. (**c**) Frame extracted from SVIRO-Uncertainty [63]. Expected behaviour of a passenger with their belongings. (**d**) Frame extracted from SVIRO-Uncertainty [63]. Objects unexpectedly abandoned in the back seat. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**Number of Frames**} & \multirow{2}{*}{**Scenes**} & \multicolumn{3}{c}{**Number of Anomalies**} & \multirow{2}{*}{**Resolution (px)**} \\ \cline{2-2} \cline{5-8}  & **Normal** & & & & & **Types** & **Total** \\ \hline UMN [58] & 6165 & 1576 & 7741 & 3 & 1 & 11 & 320 \(\times\) 240 \\ Subway [57] & 192,548 \({}^{\dagger}\) & 16,603 \({}^{\dagger}\) & 209,151 & 2 & 5 & 65 \({}^{\dagger}\) & 512 \(\times\) 384 \\ UCSD Ped1 [12] & 9995 & 4005 & 14,000 & 1 & 5 & 54 & 238 \(\times\) 158 \\ UCSD Ped2 [12] & 2924 & 1636 & 4560 & 1 & 5 & 23 & 320 \(\times\) 240 \\ CUHK Avenue [11] & 26,832 & 3820 & 30,652 & 1 & 5 & 47 & 640 \(\times\) 360 \\ ShanghaiTech [4] & 300,308 & 17,090 & 317,398 & 13 & 11 & 130 & - \\ IITB-Corridor [60] & 375,288 & 108,278 & 483,566 & 1 & 10 & - & - \\ ADOC [61] & 162,093 & 97,030 & 259,123 & 1 & 25 & 721 & 1920 \(\times\) 1080 \\ Street Scene [62] & 159,341 & 43,916 & 203,257 & 1 & 17 & 205 & 1280 \(\times\) 720 \\ UCF-Crime [40] & - & - & 13,741,393 & 1900 & 13 & - & - \\ XD-Violence [59] & - & - & - & 4754 & 6 & - & - \\ UBnormal [55] & 236,902 & 147,887 & 89,015 & 29 & 22 & 660 & 1080 \(\times\) 720 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Brief summary of relevant information regarding the analysed anomaly detection datasets. Legend: \({}^{\dagger}\)—Data computed by [PERSON] et al. [55] based on the tracks from [PERSON] et al. [64]. in this area. The identification of unattended objects was chosen as the main challenge in providing safety in public transport, after the findings of PREVENT CSA, which served as a case study for this project. The detection of abandoned or suspect objects is a part of the broader anomaly detection problem, and the proposed scenario includes in-vehicle monitoring. The final goal of this project funded by the European Union's Horizon 2020 programme is to allow buyers to steer the development of cost-effective solutions directly toward their needs. Additionally, it reinforces the competitiveness of the EU technology and industrial base by funding projects of public interest. European Union's Horizon 2020 programme is also responsible for the funding of i-DREAMS, which is a project designed to set up a framework for the definition, development, testing and validation of a context-aware _Safety Tolerance Zone_ for driving. This analysis is meant to prevent drivers from getting too close to the boundaries of unsafe operation and to bring them back into the safety tolerance zone while driving. Although the full scope of the project transcends the recognition of the abnormal activity of the passengers, the detection of internal distractions, extreme emotions or health concerns might be supported by deep anomaly detection solutions. A more traditional project, funded by the same programme, can be found in SecureIT. One of the explored domains consists of public space protection, especially at major events.