MATLAB Implementation of Object Tracking Algorithm Using Self-Learning Subspace

This paper presents an object tracking algorithm implemented in MATLAB that utilizes a self-learning subspace approach to achieve superior accuracy and computational efficiency. The algorithm's core innovation lies in its integration of particle filtering, a sequential Monte Carlo method that effectively handles noise and uncertainties in tracking scenarios through probabilistic state estimation. During implementation, the algorithm employs particle filtering by generating multiple hypotheses (particles) representing possible target states, with weights updated recursively using importance sampling. Additionally, we incorporate advanced techniques such as deep learning and convolutional neural networks (CNNs) to enhance feature representation. The CNN component typically involves pre-trained architectures like VGG or ResNet for feature extraction, followed by fine-tuning on tracking datasets. These implementations contribute to improved robustness against occlusions and appearance changes. We believe this algorithm, with its MATLAB-optimized code structure leveraging parallel computing and matrix operations, will establish a significant presence in future object tracking applications.