# Tracking Method of GM-APD LiDAR Based on Adaptive Fusion of Intensity Image and Point Cloud
## 1 Introduction  
Single target tracking is a kind of method that can predict the target position in subsequent frames on the premise of determining the specified target position information in the first frame image [1]. Single target tracking technology based on 3D point cloud has been developed rapidly in recent years. In 2019, [PERSON] et al. pioneered SC3D, a single target tracker based on shape completion networks and twin networks [2], which integrated the auto-encoder in the shape completion network into the frame of the twin network and used the encoding structure of the auto-encoder as the feature extraction network, enhancing the robustness of the single target tracking. In 2022, [PERSON] et al. proposed M2-Track [3], which introduced a motion-centered paradigm. This approach took the point cloud directly from two frames without clipping as input and split the target point from the surrounding environment to estimate the current bounding box by explicitly modeling the motion between the two frames. In 2023, [PERSON] et al. put forward a tracking network called CXTrack [4], a kind of target-centric transformer network, which directly used the point features in two consecutive frames and previous bounding boxes as inputs to explore contextual information, implicitly propagate target clues, and model theintra-frame and inter-frame feature relationships. The context information in successive frames is fully utilized to enhance the robustness to interference cases. The single target tracking method based on deep learning requires a large number of training samples. However, the data samples of the Geiger-mode Avalanche Photodiode (GM-APD) LiDAR developed by us are insufficient to support the algorithm training based on deep learning, affecting the tracking accuracy of the special category and specific model. Observing the progress of target detection and recognition in point cloud data processing, the technical concepts in the field of two-dimensional image processing are still of great enlightening significance for the corresponding research of three-dimensional point cloud [5]. In the research of 2D image tracking, discriminative target tracking method incorporates the idea of machine learning and transforms tracking into a binary classification problem. In the training stage, the target region is defined as positive samples, and the surrounding background is divided into negative samples, aiming at training a classifier that can effectively distinguish the target from the background. In the subsequent tracking sequence, the classifier is used to detect the target within the search area, and the region with the highest response value is selected as the target location [6]. [PERSON] et al. introduced the tracking algorithm based on minimum output sum of squared error for the first time in the field of target tracking [7]. By using the concept of correlation filtering, the similarity calculation of the target and the candidate area is transferred from the time domain to the frequency domain, which significantly improves the algorithm's calculating speed. [PERSON] et al. proposed a CSK tracking method [8] to which the ridge regression model is introduced generates training sample set by using circular shift technique and utilizes the characteristics of the circular matrix to improve the solving speed. Subsequently, they come up with a Kernelized correlation filtering (KCF) algorithm based on CSK tracking method [9]. It extends the grayscale characteristics of a single channel to a multi-channel histogram of oriented gradients (HOG) characteristics, significantly improving the accuracy of the tracking while maintaining the speed of the algorithm. However, the tracking effect of this algorithm remains to be improved when dealing with target occluded or scale changing. [PERSON] et al. proposed a KCF single target tracker based on RGB-D images [10], which uses the target depth data to adjust the size of a given target and detect occlusion.