## 1 Introduction  
With a large number of applications of visual algorithms in autonomous navigation of unmanned vehicles and UAV positioning, real-time tracking and positioning of 3D space targets based on vision has become a popular direction in the field of computer vision. In recent years, methods based on neural networks and deep learning have exhibited high accuracy and strong generalization capabilities for target detection and tracking. Meanwhile, various types of neural-network models have been proposed, such as the Siamese network for improving the tracking speed [1], and the Feature Pyramid Network for high-precision detection [2]. The one-stage method SSD [3] considers both speed and performance, and the once-for-all (OFA) method is for lightweight computing [4]. Besides, the emergence of unmanned driving [5] and visual slam [6] has enabled researchers to gradually expand the focus of visual research from 2D planar-image problems to 3D spatial vision [7; 8; 9]. However, it is difficult for most neural networks to achieve both accuracy and real-time due to their complex networks and a huge number of parameters. Therefore, how to effectively reduce the computational assistance of the target-detection model is an essential research direction. In many specific target-tracking applications, the 2D detection method of the target and the 3D spatial-positioning problem are generally coupled. The plane information projected by the camera is frequently employed to solve the spatial-positioning problem, while the solved spatial position can be adapted to assist the tracking algorithm of the projection plane. Similarly, can the tracking result of the projection plane be merged with the spatial depth information (or the trajectory relative to the camera) obtained by the spatial-positioning algorithm? Can it be applied as a depth parameter to join the training of the network model and finally obtain a high-precision and high-speed detection model? Based on the above consideration, a set of benchmarks for object tracking with motion parameters (OTMP) matching the sample spatial information was first designed using the indoor motion-capture system in this study to assist our algorithm research. Then, an improved model based on Single-Shot MultiBox Detector (SSD) was proposed. This model integrates the spatial-depth information of the sample into the network training and adopts the normalized depth information of the target to select the feature window and the classifier under the convolutional layer of a specific scale on the original SSD for detection. This significantly reduces the computational burden of simultaneously calculating feature channels of all scales. Finally, a target-detection and tracking algorithm framework based on the FDA-SSD monocular sensor platform was established in the combination with the author's previous target-location method based on geometric constraints [10]. Specifically, an efficient and robust geometric constraint equation method was taken to solve the target space; the normalized depth information of the target in the current frame was fed back to the FDA-SSD of the next frame to select a detector at the matching scale for target detection. Finally, the real-time target detection and tracking of the monocular vision platform was realized. Considering that the term \"target\" refers to a wide range, the \"target\" that is mainly studied in our study refers to objects that need to be positioned in monocular-vision scenes, such as doors or windows that need to be passed, obstacles that need to be avoided, etc. In the experimental part, the circular and rectangular box-like samples in the OTMP dataset were used as the \"targets\" to be tracked, and the relevant details are explained in Section 3. Although the target types in practical applications are different, the core idea of this study is to combine the spatial parameters to realize the scale selection of the detection model.