Object detection and tracking in 360 degree omnidirectional images: A review

360 omnidirectional imagery enables comprehensive scene understanding, making it highly valuable for applications such as autonomous surveillance, robotic navigation, and immersive virtual environments. Unlike conventional imagery, 360 equirectangular videos introduce unique challenges, including viewpoint variations, geometric distortions, illumination shifts, scale changes, wraparound effects, and high computational demands. This review provides the first integrated survey to jointly analyse object detection, tracking, projection strategies, and datasets in omnidirectional imagery, highlighting challenges, limitations, and future research directions to enhance accuracy, efficiency, and robustness. This review synthesises current methodologies, examining key algorithms and frameworks for panoramic detection and tracking, while also discussing projection techniques that transform omnidirectional inputs into tractable representations. Relevant datasets for training and benchmarking are reviewed to contextualise practical evaluation and reproducibility. Diverse approaches, from deep learning architectures to geometric transformations, are evaluated with attention to their strengths, limitations, and applicability to real-world scenarios. Finally, the review outlines open challenges and emerging research opportunities, providing a roadmap for advancing panoramic object detection and tracking, serving as a comprehensive reference for researchers in the field.