## 3 Results and Discussion
### Simulation Studies of QR-Code Tracking
The improvement in the tracking performance is also reflected in BER. BER in the visual MIMO communication has been significantly reduced with the use of the particle filter as in Figure 16.  
As rotation distortions don't contribute to a change of the LEA center, it is complex to evaluate their tracking performance. However, the QR code algorithm is capable of estimating the orientation using the detected finder and alignment pattern. This estimated orientation is used to reproject the detected QR code to its original position. Thus, the orientation error can be evaluated by using this estimated and actual orientation. Figure 17 illustrates the orientation error that occurs with and without the particle filter for images rotated in a clockwise and counter-clockwise direction by an angle of five degrees. It is observed that the particle filter tracking algorithm produces less BER than the system without the particle filter. The BER performance of the proposed detection and tracking algorithm over the rotational distortion is given in Figure 18. Since the QR code itself is highly robust for the rotational variance, the BER is nullified for five degrees of rotational distortions on both systems (with  
Figure 14: Sample. (**a**) Translated, (**b**) rotated, (**c**) scaled image. and without particle filter). No significant contributions have been observed with the use of the particle filter. The QR code itself is efficient in handling the rotational variance. Figure 16: Tracking performance over the +Gaussian noise in the translation motion. Figure 15: Tracking performance of the proposed method. The field of view (FOV) representation of the proposed system model is given in Figure 14:0 FOV is defined as the coverage area that is captured by the camera lens. The proposed system uses a constant FOV camera lens instead of an adjustable FOV camera lens. This produces a change in scale variations of the QR code as a function of the relative distance between the vehicles \((d)\). The image of the QR code acquired by the camera, when the vehicles are at minimal proximity \((d_{min})\) is considered an original image with the scale factor as one. With this benchmark, the images acquired at a relative distance \((d_{s})\) are scaled in accordance (scale factor \(<1\)). The BER performance of the proposed system over an increasing relative distance between the transmitter and receiver is given in Figure 14:1 It is observed that the proposed tracking algorithm produces negligible BER up to a scale factor of 0.6. This indicates that the QR code is detected even when the image is compressed by 40%. However, the QR code became undetected after the scale factor of 0.4 and elevated BER is observed. Figure 14:2 Orientation error in degrees by the tracking algorithm. Figure 14:3 Bit error rate (BER) performance in the rotation motion. Motion blurs often occurs during the higher relative motion of the transmitter and receiver vehicles. It can also occur due to vibrations created by the uneven road conditions. This makes a blur to occur predominately in the horizontal direction as shown in Figure 14:4 Hence, it is necessary to analyze the performance of the proposed system in the presence of motion blurriness. The images are blurred according to the various blur ratios (\(\mathrm{B_{r}}\)) and the BER for these images are evaluated. The blur ratio is a measure of the number of pixels blurred (\(\mathrm{P_{b}}\)) with respect to the size of the QR-LEA (\(\mathrm{P_{QR}}\)) in the blurred direction as in (1). To simulate the real-time environment, the acquired images are blurred for various pixel values in the horizontal direction (\(\mathrm{\theta_{b}=0^{o}}\)), which makes the QR-LEA size to be its horizontal length, i.e. 450 pixels (Ref Table 1). Figure 21 illustrates the blurred finder pattern of the QR-LEA for a length of 10 pixels. It is observed that the vertical edges are degraded and the algorithm may fail to detect these patterns. Figure 14:5  
Figure 14:6 Field of view (FOV) representation of the system model.