# Robust Parking Block Segmentation from a Surveillance Camera Perspective
## 4 Experiments and Results
### Image Segmentation Results  
In this section we present the results from testing both segmentation on the satellite 100 independent images and on the surveillance camera images located at the ITESM. We are presenting both results to allow improvements along the pipeline. Firstly, in the satellite image segmentation, we achieved more than 50% IoU in all models, shown in Figure 9. Figure 9a shows the results using overall accuracy. This metric achieved a notably higher score than the IoU shown in Figure 9b. Recall that in IoU we are not considering true negatives, just true positives, that is, the non segmented areas in the ground truth have no effect on improving the metric artificially. The variance of the models can be seen from the standard deviation. The most stable model (less standard deviation) was 0j-16b-0.011-0.000001r which achieved a median of 88.93 overall accuracy. The model scored second if we order them by the worse segmented example, so this model can be considered the best among those we trained. Satellite segmentation results on the ITESM images (see Figure 10) had greater variance. Nonetheless, the median remained inside two orders of magnitude of the previous results. The [PERSON] signed rank test showed that we can reject the null hypothesis that the two medians are the same. There is a significant improvement in the ITESM images using IoU. This could be explained by the absence of roof-like structures in ITESM that can produce a greater number of false positives. Also, parking lots are very well maintained and painted inside the institution in contrast with the _World_ dataset. Counterintuitively, the results are inverted when using overall accuracy and there is a significant difference. We surmise that there are many more true negative areas, meaning empty spaces on the images of the _World_ dataset. Also, given the ITESM location on a top-hill, there is also a lot of variability in the slopes that increase the false positive rate. Segmentation of the satellite images performed quite well, both at time and accuracy. However, when introducing a homography transformation, the median results dropped, shown in Figure 11. Considering this fact, we can safely conclude that any improvement in the homography calculation will greatly improve the overall result. All supervised segmentation done in _APKLOT_ follows a clear rule--mark the area wherever is visible. However, one of the known issues of evaluating a supervised segmentation task is that the  
Figure 9: Segmentation satellite results on the _World_ dataset (100 images): (**a**) is by using dlib’s measure—overall accuracy, (**b**) shows intersection over union (IoU). The bests model considering the medians (the mean is vulnerable to outliers) are for (**a**) 1j-30b-0.11-0.00001r and for (**b**) 0j-16b-0.11-0.00001r. The experiments were named according to the following naming convention: _<jittering was used>_**j**_<batch size>_**b**_<initial learning rate>_**I**_<minimum learning rate>_**r**. ground truth data is vulnerable to the subjective criteria of each person [34]. Furthermore, pixel-by pixel segmentation have many challenges like--partial occlusions, tool limitations, resolution mismatches and alike. Considering these natural limitations, perfect accuracy would only be achievable in case the two shapes, that is, the predicted and the ground truth shapes, were identical. To overcome this challenge, we have included the first boxplot of Figure 11 as a baseline. This boxplot shows the results of the satellite ground truth images with the applied homography. Finally, all the boxplots were produced using the real ground truth segmented by a human in the camera perspective. A three-number summary (first and third quartile omitted due to lack of space) of the distribution is given in Table 3.  
Figure 11: Segmentation camera results on the _ITESM_ dataset (14 + 62 images): (**a**) is by using dlib’s measure—overall accuracy, (**b**) shows IoU.