# Real-Time Motion Adaptation with Spatial Perception for an Augmented Reality Character
## 5 Experiments and Results
### Experiment 1: Object Detection Accuracy for an AR Environment  
First, we evaluated object detection accuracy to estimate the performance in a real-world situation where puddles were used as examples of the real environment. Our evaluation method determined which situations provided more accurate object detection. Figure 6 shows the accuracy degrees using the position and size of the objects in a given image of the real environment. It was found that more accurate results were obtained when the target object was in the center of the input image (see Figure 6 left). Then, in the test condition, the experiment was set up with the same sliding window size (i.e., \(50\times 50\)). We also found that the detection performance was better when smaller objects in the image were recognized (see Figure 6 right). The darker colors in Figure 6 indicate more accurate results.  
Figure 7 shows an example of the detection error in various situations. In the experiment, performance was evaluated using three confusing situations (i.e., water/puddle, slightly wet ground, and dry road). The right side of Figure 7 shows a confusion matrix of overall performance, and darker colors of the right image indicate highly confusing values in our detection scenario. This error was caused by the influence of light that occurred when the light was reflected. For example, the error probability of recognizing the wet ground in the image as an ordinary road was found to be 65%. Then, in certain situations, it was possible to determine what the object was, but it was not possible to determine exactly where the object's area was (see Figure 7 left). In our estimation method, there were many situations where our predictions turned out to be incorrect. For instance, many results incorrectly predicted water as a road. We will leave it to future research to overcome this difficulty. As the most representative result related to our research work, [PERSON] et al. focused on user-defined motion gestures of a virtual character interacting with real environments in an augmented reality (AR) environment and generated corresponding motions using a mobile AR interface [10]. In a usability study, they evaluated the accuracy and editing time for user-created character motions in given real environments. In our research work,  
Figure 5: Examples of AR interaction between a participant and a character. The AR character is represented on a portable smart pad that operates in an outdoor environment and can express the adaptive motion (e.g., suitable walking) applied to the given environment.  
we investigated the accuracy of object detection and co-presence of the AR character for automated motion generation. The next section will present the results of user experiments on co-presence.  
Figure 6: Quantitative accuracy of object detection to evaluate performance in a real-world situation using puddles as examples. These results were probabilistic recognition performance. The **top-left** side of the figure shows the performance for the location in the given image, and the **top-right** side shows the performance related to the size of the target object. Here, darker colors indicate more accurate results. The **bottom-left** figure shows the x and y position marked in red of a real image, and the **bottom-right** figure shows an example for understanding the size of the width and height. The results show that the objects had the highest scores when they were located between 0.4 and 0.6 in the image; in the case of the size, the best results were between 0.05 and 0.15. Here, numbers for experimental results represent relative normalized values.  
Figure 7: Example of our results related to detection errors. The figure shows that an error occurred due to light reflection (**left**). The **right** side shows a confusion matrix of our overall performance, where darker colors indicate higher values. For example, the error probability of recognizing the wet ground in the image as an ordinary road was found to be 65% (see the block in the bottom middle of the **right** image).