Application of a pupil tracking method based on Yolov5-Deeplabv3+ fusion network on a new BPPV nystagmus recorder

Nystagmus information is an essential basis for diagnosing benign paroxysmal positional vertigo (BPPV), and nystagmus recorders are a crucial way to obtain nystagmus information. We designed a new wireless video nystagmus recorder, which uses the OV4689 sensor to collect the nystagmus video, encodes it on the RK3399 core control board, and sends it to the host computer through the 5GHz WiFi module. Compared with the current nystagmus recorder, it has the advantages of low price, convenient operation, and high frame rate. This paper uses a novel semantic segmentation network based on the Yolov5 target detection network and the improved Deplabv3+ segmentation module to perform real-time pupil segmentation on the collected video. Based on the segmentation results, we use ellipse fitting to extract the boundary and center of the pupil for drawing the pupil activity trajectory. Compared with the existing pupil tracking method, the method divides the pupil area faster and with higher precision, ensuring the pupil center positioning accuracy. The technique can effectively assist doctors in the diagnosis of BPPV.