## 2 Methods
### Slip Detection
#### 2.3.3 Optical Path Occlusion and Remaining Background Noise
Specifically, transparent objects like glasses, bottles, and vases can lead to false positives, as background movements behind the object can be registered as slip events. On the other hand, objects without texture, such as plain mug

Figure 6:0 The ellipse estimating the object boundaries is stabilized relative to the gripper by matching a template. The black rectangles represent where in the image the template was found. The image is part of the dataset published under open access in Ref. [20]. Figure 6:1 The objects of the open access dataset [20] used for the evaluation. Seven objects had to be excluded, since the gripper left the frame during the lifting task, making it impossible to stabilize the frames relative to the gripper. and unprinted cardboard, present the opposite challenge, as no change in pixel values is detected even when the object slips. To address these challenges, the dataset was split into two groups based on the presence of texture. A total of 19 objects were categorized as textured, while 9 objects were categorized as untextured, based on subjective judgment by the conductor of the experiment. When transparent objects and objects without much texture were excluded, the algorithm's accuracy improved to 87.19%. On the excluded objects, an accuracy of 67.67% was achieved. An additional argument can be made regarding the cost of false positives and false negatives in the slip detection algorithm. While false positives may not have a significant impact as they result in the gripper grasping the object tighter, for very compliant objects, this could be problematic. On the other hand, false negatives have a much greater cost, as they can lead to the object being dropped and potentially damaged or cause the contents of the held object, such as water in a mug, to be spilled. Therefore, it is crucial to examine the recall, which indicates the percentage of actual slip cases that were correctly identified. The algorithm achieves a recall of 75.00% on the entire dataset. However, when evaluated with only textured objects, the recall increases to 93.33%. On the other hand, when tested with untextured or transparent objects, the recall drops to 50.67%. To improve the recall and reduce the occurrence of false negatives, a further optimization of the algorithm's threshold was performed. A weighted optimization was used to strike a balance between recall and accuracy. The recall was given twice the weight of accuracy, and the threshold was adjusted accordingly. The result of the weighted optimization can be found in Figure 6:2 After optimization, the recall of the algorithm increased by 13.14% to 87.14%, while the accuracy decreased by 3.57%, resulting in an accuracy of 78.81%. The greatest gain in recall was observed for untextured objects, with an increase of 12.66%. For textured objects, a smaller gain of 1.76% was achieved. ## 4 Discussion

The proposed slip detection algorithm based on classical optical flow calculations using only visual data addresses common issues like optical path occlusion and compliant objects, while also being robust to outside influences through background removal. The results of the evaluation demonstrate that the performance of the proposed method is comparable to the state-of-the-art approaches (Table 3). While other methods achieve slightly higher accuracy when including tactile data, attaching tactile sensors to the gripper might not always be feasible. Many existing methods utilize bulky parallel grippers to attach tactile sensors, which can add additional thickness to the gripper, making it less suitable for some applications. It is evident from the evaluation that the algorithm's performance is highly dependent on the object being grasped. Two main issues are apparent: First,