Evaluating Object Tracking Algorithms

Object tracking is a critical task in computer vi-sion, with applications ranging from surveillance and human-computer interaction to augmented reality and medical imaging. This paper provides a comprehensive evaluation of various state-of-the-art object tracking algorithms, focusing on their performance in challenging scenarios such as occlusion, out-of-view conditions, and illumination variations. I examine six prominent algorithms: Struck, Deep Learning Tracker (DLT), Context Tracker (CXT), Adaptive Structural Local Sparse Ap-pearance Model (ASLA), Tracking-Learning-Detection (TLD), and Distribution Fields for Tracking (DFT). my study includes the collection of a diverse dataset from the internet to test these algorithms under realistic conditions. I highlight common issues such as overfitting to initial frames and target shifts during occlusion. By analyzing the performance of each algorithm on se-lected image sequences, I identify their strengths and weaknesses. Additionally, I propose success plots and average running time as key evaluation metrics, emphasizing the importance of both accuracy and real-time applicability in object tracking.