# Object Tracking Algorithm Based on Multi-Layer Feature Fusion and Semantic Enhancement
## 3 Experimental Results and Analysis
### Datasets  
To precisely assess the strengths and weaknesses of the proposed tracking algorithm relative to other algorithms, this study chose the globally recognized LaSOT, OTB-100, and UAV123 datasets for evaluation. The video images of these datasets were collected by a single camera, which realistically simulated the real tracking scenes under fixed and non-fixed camera situations. The application scenarios are extremely wide-ranging, involving public area monitoring, natural environments, urban traffic, sports events, unmanned aerial vehicle tracking, and other realistic fields and even cover some animation scenes. Furthermore, the dataset encompasses a variety of challenging factors, including changes in illumination, occlusion, object deformation, and cluttered backgrounds. These elements effectively replicate the diverse real-world issues that can arise in tracking tasks, thus offering ample and realistic data for rigorous algorithm testing. Regarding the configuration of training parameters, the training process primarily utilized the LaSOT training set. The sampling approach involved randomly selectingtwo frames from each image sequence. In the test phase, OTB-100, LaSOT, and UAV123 test sets were selected. The reason why these datasets were selected is that their scenarios are rich and diverse, and they can test algorithm performance in an all-round way. At the same time, their memory consumption is small, which is convenient for efficient training and testing processes, and can adjust algorithm parameters flexibly and promptly according to test results. The following are the details of these datasets. 1. LaSOT [25]: Launched at CVPR in 2019, the LaSOT (Large-scale Single Object Tracking) dataset has become a cornerstone in the realm of single-object tracking, offering a demanding and representative benchmark for evaluating object tracking algorithms. This dataset comprises 1550 video sequences, which are thoughtfully divided among training, testing, and validation sets. Spanning 85 object categories, with each category containing 20 sequences, the dataset amasses over 3.87 million frames, making it one of the most extensive collections in the single-object tracking domain at the time of its release. The LaSOT dataset is distinguished by its comprehensive attribute labeling, with each sequence meticulously annotated with 14 attributes. These attributes encompass a wide range of challenges, such as illumination variation (IV), fast motion (FM), full occlusion (FOC), object deformation (DEF), partial occlusion (POC), motion blur (MB), background clutter (BC), scale variation (SV), occlusion variation (OV), camera motion (CM), rotation (ROT), low resolution (LR), aspect ratio change (ARC), and viewpoint change (VC). This detailed labeling accurately captures the diverse and complex scenarios that objects may encounter within video sequences. In constructing the dataset, the creators adhered to principles of high-quality dense annotation, long-term tracking sequences, balanced categorization, and thorough labeling. These principles ensured the dataset's high quality and richness, providing a robust and reliable foundation for single-object tracking research. As a result, the LaSOT dataset has significantly propelled advancements and breakthroughs in this field. 2. OTB-100 [26]: As a classic dataset in the field of single-object tracking, the OTB-100 (Visual Object Tracking Benchmark) builds upon the foundation of OTB-2013 by adding 50 new video sequences, bringing the total number of videos to 100. These sequences encompass 11 challenging attributes: low resolution (LR), illumination variation (IV), occlusion (OCC), fast motion (FM), motion blur (MB), in-plane rotation (IPR), background clutter (BC), out-of-plane rotation (OPR), deformation (DEF), occlusion variation (OV), and scale variation (SV). The dataset includes both grayscale and color images.