SEATrack: Simple, Efficient, and Adaptive Multimodal Tracker

SEATrack addresses the specific problem of 'parameter bloat' in multimodal tracking (RGB + Thermal/Infrared/Depth). While many recent PEFT (Parameter-Efficient Fine-Tuning) trackers have ironically become quite heavy, SEATrack focuses on cross-modal alignment and adaptive fusion to maintain efficiency. From a competitive standpoint, the project currently has 0 stars and 6 forks, indicating it is likely a brand-new research release (as of 1 day ago) with initial interest from the academic community rather than industrial users. The defensibility is low (3) because, despite the technical novelty in the alignment mechanism, it is a reference implementation of a research paper. It lacks a surrounding ecosystem, data gravity, or commercial-grade tooling. It competes with established trackers like OSTrack, ViPT, and BATMAN. Frontier labs (OpenAI/Google) are a medium risk; they are building general-purpose multimodal models (like Gemini or GPT-4o) that could eventually handle tracking as a zero-shot capability, but the specific niche of high-frequency, resource-constrained multimodal tracking for robotics or surveillance remains a specialized field for now. The displacement horizon is relatively short (1-2 years) because the state-of-the-art in tracking shifts rapidly with each CVPR/ICCV cycle.