Visual Object Tracking Algorithm Based on Deep Learning

In the rapid development of machine vision, visual object tracking is an important research topic. Traditional methods are difficult to effectively solve problems such as complex backgrounds, lighting changes, and object occlusion. Therefore, this paper intends to introduce deep learning(DL) technology into image processing, automatically extracting and mining high-level features of objects through learning from massive image data, in order to achieve accurate tracking. This study focuses on data collection and preprocessing methods for public data (OTB (Object Tracking Benchmark), VOT (Visual Object Tracking), etc.), using techniques such as image cropping, scaling, normalization, and data augmentation to ensure data consistency and diversity. On this basis, research is conducted on target tracking methods based on DL, including neural network framework selection, training process, online learning and model updating mechanism, multi-scale tracking strategy. By comparing and analyzing methods with traditional evaluation mechanisms such as accuracy, success rate, tracking speed, etc., a comprehensive analysis of the algorithm's performance is conducted. Meanwhile, this paper will also compare the performance of DL in different scenarios through a series of experiments. Under unobstructed conditions, the DL algorithm has the best performance, with an accuracy of nearly 90%, while the performance of traditional algorithms is about 20 percentage points lower. The research results of this paper will enrich vision based object tracking methods and provide important support for in-depth research and application in related fields.