## 3 Results
### Tracking System Evaluation  
The final detector generated in the previous experiment (see Section 3.1) is embedded into a simple tracking system. Its detections are passed to a Kalman filter to estimate the detected workers' movements, as explicitly described in Section 2.4. By means of the developed tracking system, we investigate the suitability of computer generated 3D scenes for the evaluation of computer vision-based tracking systems. For this, we compare its tracking results on real and synthetic construction site scenarios. Our tracker is compared by means of the two different real world scenarios (see Section 2.1) and their computer generated counterparts described in Section 2.2. From each scenario we extract a continuous 10 second video sequence in which four pedestrian workers move across the scene varying their pace and direction in each sequence. All video sequences also include sudden stops as well as pedestrian workers coming close to and crossing each other. In the real world sequences, ground truth is labeled manually whereas this is done automatically (see Section 2.2) for the computer generated scenes. We contrast the accuracy of our tracker on each of these video sequences by the average overlap score (AOS) and the center location error ratio (CER) metrics while its robustness is measured by the track length (TL). These metrics are adapted from [32] as shown in Equations (3) to (5). \[\text{AOS} =\frac{1}{n}\sum_{t=1}^{n}\frac{A_{t}^{\text{G}}\cap A_{t}^{ \text{T}}}{A_{t}^{\text{G}}\cup A_{t}^{\text{T}}}, \tag{3}\] \[\text{CER} =\frac{1}{n}\sum_{t=1}^{n}\frac{||C_{t}^{\text{G}}-C_{t}^{\text{ T}}||_{2}}{\text{size}(A_{t}^{\text{G}})},\] (4) \[\text{TL} =\frac{n}{N}, \tag{5}\]  
where \(n\) and \(N\) denote the number of video frames in which a worker is tracked and the number of frames in which the worker is present, respectively. The workers' bounding box areas are indicated by \(A\) and their centers by \(C\). The superscripts \(G\) and \(T\) indicate ground truth and tracked bounding boxes, respectively. Lastly, \(||\circ||_{2}\) denotes the two dimensional euclidean distance and \(\text{size}(\circ)\) represents the two dimensional size of an area. Figure 4: Increase of YOLO’s detection quality by successively adding synthetic data samples. Tables 2 and 3 summarize the tracking results on the first and second pair of scenes, respectively. As can be seen, in each of the sequences our tracker identifies four tracks. Each of these corresponds to one of the pedestrian workers as illustrated in Figure 5 for all sequences. No tracks are set up mistakenly tracking other objects in the scenes. With an average of 96 % in AOS and a very low CER on both real world scenes, our tracking system yields an accurate performance. In addition, the averages in TL of 97 % and 98 % highlight the systems' robustness. Deviations from the optimal TL are mainly due to the fact that it takes a few frames until sufficient consecutive detections are identified to start a track. In isolated cases, starting a track was delayed since the initial detection was complicated by odd lighting conditions. The measurements' averages of the synthetic scenes are slightly lower than those of the real world scenes, but still demonstrate an accurate performance of the tracking system. \begin{table}
\begin{tabular}{c|c c c c|c c c c c|c} \hline \hline  & \multicolumn{5}{c}{**Real Scene 1**} & \multicolumn{5}{c}{**Synthetic Scene 1**} \\ \hline ID & 1 & 6 & 7 & 8 & AVG & 0 & 1 & 2 & 3 & AVG \\ \hline AOS & 0.91 & 0.96 & 0.99 & 0.98 & 0.96 & 0.94 & 0.90 & 0.94 & 0.90 & 0.92 \\ CER & 0.0017 & 0.0015 & 0.0014 & 0.0017 & 0.0016 & 0.0013 & 0.0020 & 0.0018 & 0.0033 & 0.0021 \\ TL & 0.98 & 0.92 & 0.99 & 0.99 & 0.97 & 0.99 & 0.90 & 0.94 & 0.98 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of our system’s tracking performance on the first scene from our real dataset and its synthetic counterpart. IDs are assigned randomly and correspond to a single tracked worker each.