## 1 Introduction  
In recent years, with the development of robot technology, robot vision has become the forerunner of machine vision, and vision has also become one of the most important sensing technologies for robots [1, 2]. Target grasping is one of the most typical applications in robotics. The combination of machine vision and robot control provides a feasible solution for tracking and grasping dynamic targets [3, 4]. For multi-target object grabbing on low-speed conveyor belts, [PERSON] et al. [5] extracted local features (via SIFT) from the visual image sequences of a target and mapped the key points of each image to a unified dimensional histogram vector. However, SVM classifiers require much offline training to achieve good results, and a large number of SIFT feature points of sample images need to be extracted during training. [PERSON] et al. [6] considered an image block as a second-order tensor and constructed a discriminant embedding space. They mapped the original tensor sample to the tensor-based graph embedding space. The semi-supervised strategy was applied to visual tracking. Some researchers adopted the strategy of block modeling and tracking. For detection and tracking in the case of target occlusion, [PERSON] et al. [7] followed the fragment modeling idea in [8], fused the idea in [9] and applied a horizontal and vertical axis slicing method to modeling. It solves the tracking problem for full occlusion. However, for background clutter in complex environments, there is still a problem with its tracking performance. [PERSON] et al. [10] proposed a block modeling method based on color distribution. It improved the adaptability to complex situations such as illumination variation. Other researchers focused on multi-dimensional moving target tracking. [PERSON] et al. [11] proposed a three-dimensional attitude tracking method that combined \"1-Step GA\" and manual feedback compensation. It was different from the optimized tracking method based on Taylor expansion and did not fall into problematic local minima. [PERSON] et al. [12] used two CCDs as stereo vision sources to separate targets from paired images. They calculated the centroid coordinates of each target image and determined the space position of targets. The Lagrange interpolation formula and linear function were used to simulate the trajectory of the ball, and the catching position was predicted. [PERSON] et al. [13] used the OptiTrack system to capture the target ball and first learned the trajectory of the parabola via a machine learning algorithm. The computer can quickly predict the trajectory of the target when grasping. It indirectly achieves recognition and tracking through the target ball on the target, which has certain limitations. [PERSON] et al. [14] studied the model-based three-dimensional tracking problem with dense depth images as input. They used the Gaussian filter robustification method to address the problem of the thick-tail measurement noise of the depth sensor. Experiments indicated that the performance was better than that of the standard Gaussian filter and particle filtering (PF) tracking methods. Many researchers used the method of matching and probabilistic prediction and tracking in their research for complex environments with limited viewing angles. [PERSON] et al. [15] used the distributed learning ability of a generative adversarial network (GAN) to propose a general multi-agent probabilistic prediction and tracking framework. The framework considered the interaction between multiple entities and treated all entities as a whole. However, as GAN training was very difficult, the applicability of the algorithm was reduced. [PERSON] et al. [16] proposed a new hierarchical hidden Markov model and an online algorithm for learning the parameters from observed data collected during operation. A nested particle filter used robot observations to measure the weight of nested particles while self-locating and tracking another robot. [PERSON] et al.