# Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data
## 5 Conclusions and Future Work  
In this paper, an approach towards developing a general system for classification and counting of vehicles on ordinary webcam pictures is presented. Global traffic is steadily increasing which raises the need for intelligent traffic measurement systems. Due to the fact that modern computer systems can now process a large amount of data in real time there are new ways of developing surveillance applications. Furthermore, all-time connected devices such as camera systems on the road can deliver traffic scenes on the fly which can be processed automatically by autonomous systems. As an alternative to induction loops a more generic and flexible approach for metering traffic is presented. Based on remote accessed cameras which deliver live-feeds from certain traffic hotspots a computer application analyses frame by frame. Therefore, the proposed system uses common computer vision and machine learning techniques in a hybrid application. The tracking and counting is of special interest due to the fact that the system operates in a restricted environment, e.g., processes video data with low frames per second. The proposed application is built up using different, independent modules which all contribute to the overall classification and counting system. It is organized in a never ending system loop beginning with the gathering of data from an input feed reaching to the counting of vehicles based on their types. One of the main modules is the detection and classification framework. Therefore, a state-of-the-art Convolutional Neural Network (CNN) is used. This framework is called YOLO which stands for \"You Only Look Once\" and is an open source implementation of the Darknet suite. Depending on the installed hardware YOLO is capable of classifying multiple objects within a single frame in real-time. Additionally, it can also deliver the exact position and size in the frame of all the detected objects in the very same step, hence it has to look on the image only once. YOLO is outstanding regarding object classification and detection in terms of computation time. Another key component of the proposed system is the prediction model. The goal of this module is to estimate the next position of every object from the current frame to the following one. Prediction modelling for a tracking framework is not always necessary. However, the system has to face special requirements in real-world scenarios which have great effect on the tracking performance. Due to the fact that the system should work with different kinds of road types and traffic directions, predicting the next position limits the search space for tracking. Hence, it is often needed to find the same object in the following frames. Furthermore, video data from remote camera accesses are often restricted in bandwidth. This fact leads to quality issues regarding the image resolution, sharpness or reduced frames per second. For the latter objects passing by cover big distances between the separate frames which have great impacts on the tracking performance needed for counting. Basically, estimating the next position or state of an object adds additional information to the raw image data which helps the tracking software to improve its performance and stability. To overcome the mentioned problems the prediction model tries to estimate driving paths without the need for user interaction or specific settings. Based on several assumptions the prediction model adapts to the different traffic scenes automatically. In the initial phase of the systems life-cycle the model initializes itself with the detections from the detection and classification component. The prediction model saves all the positions from the detections with additional data such as the frame count. If the model has enough data points from all the past frames it tries to make associations between these points. The aim of the prediction model is to find possible driving paths in successive frames.