# Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data
## 5 Conclusions and Future Work
### Outlook  
To develop a more solid system which is able to deal with this versatile environment, several improvements need to be considered. This can be done as a future research to reach a more stable and less error-prone system. The following list describes suggested improvements related to discovered defects within this paper in more detail:  
* _Detection and classification_ First of all, the detection and classification framework has to improve its performance in regard to small sized vehicles. This can either be done by running the system with a better hardware (GPU) or by training the model specifically for small vehicles. Furthermore, training the framework with samples from different weather conditions may also have an positive effect of the detection and classification performance.
* _Prediction model_ Estimating the driving paths only by the raw detections from the preceding frames leads to too many different possible tracks, hence resulting in wrong estimations and tracing of objects. Therefore, enriching these data with additional information helps to estimate the right driving paths. Another idea would be a post-processing step while updating the prediction model. A filter could remove wrongly detected paths with the help of the already established ones. Furthermore, added driving paths could be split up into different categories in regard to the driving direction.  
If the traffic is not equally distributed between the lanes it often leads to wrong estimations for the less frequent driving way because they are already outdated.
* _Tracking_ Tracking of objects relies mostly on the detections and predictions from the previous modules. To improve the overall performance the tracking process could additionally consider other facts as well, e.g., driving direction, visual appearance or position on the screen. As a result the errors while matching cars from the different frames could be reduced.
* _Counting_ The counting process of vehicles could be done not only based on vehicle types but also based on the driving direction. This is especially useful for highways to estimate possible congestions and the corresponding direction.  
The proposed scheme has been developed within the context of a research project, which has ended shortly prior to the public release of the new YOLO version 4, which includes new additional methods which most probably lead to a further increase in classification, detection and counting rates. YOLOv4 uses the CSPDarknet53 as backbone network, which includes a spatial pyramid pooling block to increase the receptive field and separate out the most significant context features. A follow-up research project regarding not only the use of YOLOv4, but also a comparative study within the context of alternative frameworks such as those provided by Matlab, is planned for 2021.  
Conceptualization, O.B., E.S.; software, O.B.; project administration, E.S.; supervision, E.S.; writing-original draft preparation, O.B.; writing-review and editing, M.K., E.S., A.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This paper and all corresponding results, developments and achievements are a result of the _Research Group for Secure and Intelligent Mobile Systems_ (SIMS).  
The authors declare no conflict of interest.