# Object Tracking Algorithm Based on Multi-Layer Feature Fusion and Semantic Enhancement
## 1 Introduction  
Object tracking technology primarily involves analyzing dynamic objects in video or image sequences to achieve the accurate estimation and prediction of their continuous states. Object tracking technology typically relies on deep learning methods, which train models to recognize the features of objects and track their motion trajectories. Research on object tracking not only drives technological advancements in the field of computer vision but also serves as a fundamental support for a wide range of applications, including automated surveillance, autonomous driving, interactive media, and augmented reality. At present, deep learning-based tracking methods are primarily categorized into two types: fully convolutional Siamese networks and Transformer-based tracking approaches [1]. Siamese networks utilize a dual-branch structure with shared weights to achieve fast and precise similarity matching. The SiamFC [2] network was the first to introduce a fully convolutional architecture for single-object tracking, achieving efficient end-to-end tracking. Subsequent works such as SiamRPN [3] and DaSiamRPN [4] enhanced model robustness by incorporating region proposal networks and optimizing the backbonestructure. SiamRPN++ [5] and SiamMask [6] further improved feature fusion and task integration to better adapt to complex scenarios. SiamFCA [7] applies local histogram equalization to each patch to enhance image contrast and suppress noise, while employing an improved AlexNet to extract deep features and introducing a coordinated attention mechanism, enabling the more accurate modeling of relationships between different objects and improving performance in complex visual scenes. Despite significant advances in tracking accuracy and speed, Siamese networks still face challenges in handling fast motion or heavily deformed objects, as well as maintaining stability and efficiency in long sequences [8]. To enable comprehensive analysis and understanding of the input data, Transformer models have been introduced into various stages of the tracking pipeline, serving both as modules for specific functions and as the foundation for overall tracking frameworks. Transformer-based tracking methods leverage the attention mechanism to establish long-range dependencies in spatial and temporal dimensions, enhancing the modeling capacity for dynamic object variations. In terms of temporal information modeling, STARK [9] and ProContEXT [10] incorporate online updating and context-aware modules to improve adaptability to object changes. VideoTrack [11] further optimizes the representation of temporal features by integrating a hierarchical structure and attention mechanisms. SwinTrack [12] integrates a Transformer architecture and motion tokens into the Siamese framework to enhance feature interaction and tracking robustness by incorporating temporal context. For spatial modeling, SiamTPN [13] uses a pyramid structure to fuse multi-layer features, improving adaptability to objects of varying scales. HiFT [14] utilizes feature modulation to enhance the recognition capability for small objects. MixFormer [15] constructs its framework through progressive embedding and a localization head, introduces an asymmetric attention mechanism to reduce computational cost, and designs an effective score prediction module for high-quality template selection. TrDimp [16], TaTrack [17], and MMT [18] enhance model robustness under extreme conditions by introducing multi-mask mechanisms. DropMAE [19] optimizes spatial information processing through a mask reconstruction approach that reduces redundancy and strengthens temporal information alignment.