This chapter explores the future directions of visual object tracking (VOT), emphasizing emerging techniques that address key research challenges. It highlights the importance of pre-trained large-scale models and the shift toward parameter-efficient fine-tuning (PEFT) for effective model transfer and adaptation. Long-term tracking is discussed in the context of real-world scenarios, focusing on re-detection mechanisms and semantic understanding for robust performance in dynamic environments. The chapter also examines lightweight model design for deployment in resource-constrained settings, unsupervised tracking approaches that leverage unlabeled data to reduce annotation costs, and few-shot tracking techniques that enable fast adaptation with minimal samples. Together, these topics reflect the ongoing evolution of VOT toward greater generalization, efficiency, and adaptability. The chapter is structured around these five major themes, each addressing both the current challenges and the promising advancements driving the field forward.