In this paper, based on the improved DeepSORT algorithm, four target species of passing fish (Schizothorax o’connori, Schizothorax waltoni, Oxygymnocypris stewartii and Schizopygopsis younghusbandi) from a fishway project in the middle reaches of the Y River were used to achieve dynamic identification and automatic counting of passing fish species using fishways monitoring video. This method used the YOLOv5 model as the target detection model. In view of the large deformation by fish body twisting, the network structure of the re-identification (ReID) model was deepened to strengthen the feature extraction ability of the model. It was proposed to identify and track fish that cross the line by setting a virtual baseline to achieve the dynamic identification of fish species passing and the automatic counting of upward and downward quantities. The results showed that 1) among the five models, YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x, the highest value of mean average precision (mAP) was 92.8% achieved by the YOLOv5x model. Specifically, recognition accuracies of 96.95%, 94.95%, 88.79%, and 91.93% were recorded for Schizothorax o’connori, S. waltoni, S. younghusbandi and O. stewartii, respectively. 2) The error rate of the improved ReID model was 20.3%, which was 20% lower than that before the improvement, making it easier for the model to obtain target features. 3) The average accuracy of the improved DeepSORT algorithm for counting four target fishes was 75.5%, among which the accuracy of Schizothorax o’connori, S. waltoni, S. younghusbandi and O. stewartii were 83.6%, 71.1%, 68.1%, and 79.3%, respectively. Meanwhile, the running speed was 44.6 fps, which met the real-time monitoring. This method is the first to implement intelligent identification of the target passing fish in fishways projects, which can accumulate long series monitoring data for fishways operation and management and provide a technical solution and reference for the work related to the realization of intelligent and informative passing fish monitoring.

Within the context of health informatics, this article discusses how real-time information of human skeleton movements can be conveniently captured via the use of Kinect-based deep sensor. It highlights an effective action recognition method, using Unity3D to form virtual models of characters and scenes. Kinect somatosensory cameras can now identify one's motion data and provide feedback on the virtual role model to complete the avatar drive. This is to achieve real-time simulation modeling as well as drive operation instructions based on the design and identification verification to complement limb rehabilitation. The hardware of such a health informatics-related system is simple, inexpensive, and highly meaningful in term of augmenting user experience. In conclusion, the suggested approach can contribute significantly towards aiding physical rehabilitation therapy

Within the context of health informatics, this article discusses how real-time information of human skeleton movements can be conveniently captured via the use of Kinect-based deep sensor. It highlights an effective action recognition method, using Unity3D to form virtual models of characters and scenes. Kinect somatosensory cameras can now identify one's motion data and provide feedback on the virtual role model to complete the avatar drive. This is to achieve real-time simulation modeling as well as drive operation instructions based on the design and identification verification to complement limb rehabilitation. The hardware of such a health informatics-related system is simple, inexpensive, and highly meaningful in term of augmenting user experience. In conclusion, the suggested approach can contribute significantly towards aiding physical rehabilitation therapy