Pearson's Correlation Coefficient for Discarding Redundant Information in Real Time Autonomous Navigation System
Lately, many applications for control of autonomous vehicles are being developed and one important aspect is the excess of information, frequently redundant, that imposes a great computational cost in data processing. Based on the fact that real-time navigation systems could have their performance compromised by the need of processing all this redundant information (all images acquired by a vision system, for example), this work proposes an automatic image discarding method using the Pearson's correlation coefficient (PCC). The proposed algorithm uses the PCC as the criteria to decide if the current image is similar to the reference image and could be ignored or if it contains new information and should be considered in the next step of the process (identification of the navigation area by an image segmentation method). If the PCC indicates that there is a high correlation, the image is discarded without being segmented. Otherwise, the image is segmented and is set as the new reference frame for the subsequent frames. This technique was tested in video sequences and showed that more than 90% of the images can be discarded without loss of information, leading to a significant reduction of computational time necessary to identify the navigation area.