## 3 Dataset
### Multi-View Image Extraction  
Due to the dwarf and densely planted plantation in the area, the jujube trees are close to each other, and there are cases of mutual occlusion, scale change, and transient loss of the target field of view. Target detection is also an important part of efficiently and accurately collecting multi-view images of the jujube garden. In this paper, Accurate Scale Estimation for Robust Visual Tracking (DSST) is used to accurately track the target of jujube trees according to the difficulties of the actual task. DSST algorithm is an improvement of the Kernelized Correlation Filter (KCF), which has the advantages of a simple algorithm, excellent performance, strong portability, etc. DSST algorithm introduces a multi-feature fusion mechanism, which combines HOG (Histogram of Orientation Gradient) features, CN (Color Name) features, and grayscale features, and adopts the way of adding scale filters to adapt to the scale change of the target during the tracking process. The DSST algorithm uses a Translation Filter to predict the location of the target in the next frame, a Scale Filter to adaptively estimate the target scale, and a Response Score is obtained through the correlation filter operation, with the maximum value being the current target location and scale [32]. The specific operational steps for dataset acquisition are as follows:  
1. Use a position filter for position estimation of the tracked target. Obtain the correlation filter ft for a certain position through training and use the filter to predict the target position in the next frame. When all the image blocks in the d-1 frame are operated with the filter, the maximum value of the response score of the image block is the estimation of the new position of the target, and finally, the parameters of the ftd filter are updated, and read into the next frame, and repeatedly iteratively updated until the end of the tracking, and the schematic diagram of the training process is shown in Figure 8. In which the response at the center of the target is the largest, and then gradually decreases from the center to the surrounding area, and finally according to the location of the maximum value of the response, thus realizing the accurate positioning of the target. 2. Use a scale filter for scale estimation of the tracked target. To overcome the problem of target scale change of \"near target is big, the far target is small\", a one-dimensional scale filter is created to estimate the scale in the image, extract the features at the center position of the target, and calculate the training samples for updating the scale filter. In the actual tracking process of the orchard, the position transformation of the target in two consecutive frames is larger than the scale transformation. Therefore, the DSST algorithm firstly captures the position information of the target in the current frame through the position filter and, secondly, captures the scale information of the target by using the scale filter and the schematic diagram of the training process is shown in Figure 9. The response is largest at the center point and decreases to both ends, and the scale corresponding to the largest value of the response is the result of the final scale estimation of the target. 3. Use keyframe automatic extraction algorithm for target multi-view image extraction. Determine the fruit tree target to always keep in the center of the screen as the key frame selection conditions, on the basis of the DSST target tracking algorithm, realize the tracking and keeping of the target, and extract the multi-view image of a single jujube tree at equal intervals, and the same operation traverses all the jujube trees. 4. Combine the sensor data to construct the 3D reconstruction dataset. The automatic extraction algorithm extracts the target multi-view image data outside. The camera outputs focal length, position, and view angle information, and the scale filter outputs target scale information.