#$%^&*AU2020100044A420200213.pdf#####ABSTRACT With the wide application of augmented reality navigation system in minimally invasive surgery, growing concerns have been put into the target tracking. Due to the complex situation of surgery and real-time requirements, most extant algorithms do not work effectively. By considering the occultation, deformation, and reflection, this invention patent ensures the robustness tracking for both surgical tools and targets based on superpixel segmentation and character description. Meanwhile, it gives a more accurate contour of surgical tools instead of a region of interest by the combination of KCF, Region growing algorithm. First, traditional TLST model can be optimized into TLST-I by only traversing ROI instead of the whole image and classifying superpixels into three types according to gradient, which is convenient for dealing with singular point and obviously reduces the complexity of calculation.Second, TLST-I can integrate image similarity weight model to detect target, while using DSST achieves scale invariant. This multi-model TSTL algorithm has a good performance on surgical target detection.Third, to detect surgical tool, region-growing is applied within ROI detected by KCF to obtain the contour of target.Finally, to make the tracking process more robust, it is possible to verify the result, after obtaining the contours of the target, according to a novel feature extraction method, making contours more accurate. 1Optimized TLST_ INPUT Multi-model TSLT mel a ROI segmenttin _ weg hting modelRsl selection ROI Clu~st otourSmi-rt Multi-ROI selection KCF Region- Sampale __ Feature algR groDwing points extraction Tool detection TT ROI Cluster Contour Similarity detection measurement Figure 1 Figure 2
