## 3 Experimental Setup and Result Analysis
### Result and Analysis
#### 3.2.2 Robustness Performance  
Robustness performance reflects the stability of the proposed tracking algorithm, which is the first step, or rather, the premise of realizing communication in a CMOS-based VLC system. As for a CMOS-based VLC, shielding effect, background interference, and interference of similar targets are the main obstacles for a tracking algorithm and even affect the success of tracking and extracting the target signal source LED area, causing communication failure. The three influencing factors mentioned above correspond to the actual scenario, so they are added into our experimental environment to test the robustness of the proposed tracking algorithm. The video recorded with a green LED signal source is used to display experimental results in this part, and the detailed results are as follows.  
First, when the target signal source LED is complete and no interference occurs, the tracking target is selected and the search window is initialized. At the same time, the Kalman filter is initialized. As can be seen from Figure 11, the proposed algorithm tracks the target signal source LED accurately with small errors and the region of the target can be extracted successfully and completely. In other words, the proposed algorithm possesses a high tracking accuracy in this situation.  
Figure 11: Target signal source LED selection and tracking algorithm initializing without interference.  
Figure 10: The cumulative distribution function (CDF) of the tracking error.  
Moreover, when the target signal source LED is masked, its valid color features information gradually decreases, making the search window shrink at the same time. The window would shrink to a minimum and stay in a local area if using the traditional Cam-Shift algorithm, causing it to be unable to track in time when the target appears completely again in future frames. Meanwhile the proposed algorithm will determine that occlusion occurs if the search window is reduced to a certain value, as shown in Figure 12. In this situation, it will use the predicted value of the Kalman filter as the measurement to update the Kalman filter and outputs the final tracking results.  
Furthermore, similar target interference and background interference occurred. A light green round LED, whose shape is the same as the target signal source LED and color is similar, entered the field of view of the industry camera. Furthermore, the background of the outdoor part appeared in the images due to reflecting sunlight, which has strong luminance and cannot be eliminated by adjusting the exposure of the industry camera. When the target signal source LED approaches the LED with similar color and severe background interference occurs, the traditional Cam-Shift algorithm would mistakenly consider the interfering LED or the background as the target and expand its search window, causing a tracking failure. The proposed algorithm can adjust its search window, shrink it, and use the Kalman filter predicted value as the measurement to update the Kalman filter when a similar color interference occurs until the target signal source LED leaves the similar background area. As can be seen from Figure 13, the extracted region contained the whole target, in other words, the proposed algorithm tracks accurately and has good robustness when interference occurred, extracting the target signal source LED area accurately, and laying foundations for communicating.  
Figure 12: Tracking performance of the proposed algorithm when occlusion occurs: (**a**) frame number: 14, (**b**) frame number 21, (**c**) frame number 34, and (**d**) frame number 47.