# Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data
## 3 Approach
### Core Components
#### 3.3.2 Detection and Classification
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Key** & **Description** & **Value** \\ \hline \multirow{2}{*}{namesFile} & File location of the names file for each classifiable object & \\  & type. Is needed for the detection framework & String (absolute path to file) \\  & while classifying. & \\ \hline \multirow{2}{*}{configFile} & File location of the configuration file needed for the & \\  & detection framework for initialization. & String (absolute path to file) \\ \hline \multirow{2}{*}{weightsFile} & File location of the weights file needed building up the & \\  & detection framework neural network. & String (absolute path to file) \\ \hline \multirow{2}{*}{remote} & Indicator if remote file location or local file location & \\  & should be used. & \\ \hline \multirow{2}{*}{videoFile} & File location of a video file, e.g., mpt file format. & String (absolute path to file) \\ \hline \multirow{2}{*}{remoteFile} & Link to a remote file location that can be requested for a & \\  & new frame. & \\ \hline \multirow{2}{*}{requestInterval} & Interval for requesting a new frame. & Integer (in milliseconds) \\ \hline \multirow{2}{*}{showHistory} & Indicator if tracking history with additional object & \\  & thumbnails should be shown or not. & \\ \hline \multirow{2}{*}{manualMode} & Indicator for disabling request interval and waiting for & \\  & manual key input by the user to get the next frame. & \\ \hline \multirow{2}{*}{showPrediction} & Indicator for showing also predictions in a & \\  & separate window. & \\ \hline \multirow{4}{*}{detectionThreshold} & Used to filter objects with confidence factors from & \\  & classification. Objects with a confidence factor less than & \\ \cline{1-1}  & the detection threshold are not considered by the system & \\ \cline{1-1}  & for further processing. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Configuration file entries with additional information. The design of the network is a typical Convolutional Neural Network (CNN) and was inspired by the GoogLeNet ([[https://ai.google/research/pubs/pub43022/](https://ai.google/research/pubs/pub43022/)]([https://ai.google/research/pubs/pub43022/](https://ai.google/research/pubs/pub43022/))) model for image classification as described in [24]. Furthermore, extracting features is done by the initial convolutional layers, whereas the fully connected layers are responsible for predicting output probabilities as well as the bounding box coordinates. Figure 3 shows the full network architecture of YOLO. It consists of 24 convolutional layers followed by 2 fully connected ones. At the end, there is simply a \(1\times 1\) reduction layer followed by \(3\times 3\) convolutional layers. The improved versions YOLOv2 and YOLOv3 have a slightly different network architecture in terms of the number of layers. The final output for the network is a \(7\times 7\times 30\) tensor of predictions. It is encoded as a \(S\times S\times(B*5+C)\) vector with \(S=7,B=2\) and \(C=20\). This tensor was used for evaluating the first version of YOLO on the PASCAL VOC ([[http://host.robots.ox.ac.uk/pascal/VOC/](http://host.robots.ox.ac.uk/pascal/VOC/)]([http://host.robots.ox.ac.uk/pascal/VOC/](http://host.robots.ox.ac.uk/pascal/VOC/))) dataset. An example of a prediction using the YOLO framework is shown in Figure 5. Corresponding to [PERSON] and [PERSON] [26], due to its architecture YOLO tries to predict several different bounding boxes simultaneously where an object is suspected. Attached with multiple class probabilities for each type ranging from 0 to 1 this output can be filtered with a specified threshold. The final outputs are then bounding boxes with object class values over a specific threshold. Conforming to [26], YOLOv3 is able to use multilabel classification on single bounding boxes. This is very useful when evaluating the framework with datasets including many overlapping labels, i.e., woman and person for the same object.