# Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data
## 1 Introduction  
To develop a suitable traffic management or surveillance system the current traffic on the road in a specific area has to be identified and measured respectively. There are several traffic measurement systems already in use providing data about the current traffic situation. However, such systems are typically inflexible in terms of accurate positioning in newly formed traffic hotspots. As an example, induction loops are built into the road directly where each vehicle driving on this induction loop is counted accordingly. Such induction loops are quite costly when large areas are covered (depending on the desired objective). Furthermore, repositioning is impossible, hence induction loops are difficult to modify once they are encased in the street.  
Contemporary camera systems in combination with appropriate software for data use offer new ways of traffic measurement implementations. In principle, camera systems can be placed somewhat arbitrarily (depending on existing infrastructure to mount the cameras) on the road establishing a more versatile and cost effective approach for traffic measurements. To develop a camera-based traffic measurement system which is capable of tracking, counting and classification of different vehicle types, a computer system has to analyze video frames using common computer vision and machine learning techniques in a hybrid application.  
The primary goal of the proposed traffic measurement system is to generate additional data for a congestion detection system. Measuring traffic with a camera-based solution has the great benefit of using already installed road surveillance cameras by public organizations. Additionally, if there are no camera systems available for a specific point of interest, such devices are easy to install as well. These cameras can usually be accessed remotely, hence it affords processing video streams from acentralized computer system. It also allows changes between common traffic hotspots in seconds by simply switching the input stream to another camera.  
The success of such a system mainly depends on five identified factors following the approach of using remote cameras:  
* The view angle is influencing perspective, size and driving direction of vehicles
* Image quality changes per camera due to qualitatively different camera systems
* Bandwith restrictions of installations influence both image quality and frame rate of the video feed
* The environment itself vastly changes over time with day and night cycles as well as weather conditions affecting visibility
* The traffic itself underlies variation concerning the speed, amount and direction of vehicles  
This paper presents a system which is capable of dealing with the difficulties and problems stated previously. In particular, low frame rate scenarios are of special interest and need to be dealt with accordingly, since the current predominant sources of video material for traffic monitoring are susceptible to that property. A prediction model is established to enrich the raw video data with additional information. It tries to find associations between the current and past detected vehicles in order to derive driving paths.  
The contributions of this work are structured as follows: A related work section gives an overview of state-of-the-art patterns and algorithms concerning computer vision and machine learning useful to the envisioned system. Furthermore, deployment scenarios from other sources similar to the proposed system are presented. The approach section describes the core architecture of the designed tracking and counting system and gives detailed insights into the actual implementation. A system evaluation section concludes the research and determines the robustness of the system under certain conditions and environments in real-world scenarios. A final conclusion and remarks about the system are given in the last section in order to provide a base for discussion, further research and future system revisions.