The forecasting of entity trajectories at future points in time is a critical
capability gap in applications across both Commercial and Defense sectors.
Transformers, and specifically Generative Pre-trained Transformer (GPT)
networks have recently revolutionized several fields of Artificial
Intelligence, most notably Natural Language Processing (NLP) with the advent of
Large Language Models (LLM) like OpenAI's ChatGPT. In this research paper, we
introduce TrackGPT, a GPT-based model for entity trajectory forecasting that
has shown utility across both maritime and air domains, and we expect to
perform well in others. TrackGPT stands as a pioneering GPT model capable of
producing accurate predictions across diverse entity time series datasets,
demonstrating proficiency in generating both long-term forecasts with sustained
accuracy and short-term forecasts with high precision. We present benchmarks
against state-of-the-art deep learning techniques, showing that TrackGPT's
forecasting capability excels in terms of accuracy, reliability, and
modularity. Importantly, TrackGPT achieves these results while remaining
domain-agnostic and requiring minimal data features (only location and time)
compared to models achieving similar performance. In conclusion, our findings
underscore the immense potential of applying GPT architectures to the task of
entity trajectory forecasting, exemplified by the innovative TrackGPT model.

TrackGPT 是一种基于 GPT 的模型，可用于实体轨迹预测，并在测距和空间两个领域表现出强大的性能，同时保持领域无关性和最小化数据特征要求。

TrackGPT -- 用于跨领域实体轨迹预测的生成式预训练转换器

TrackGPT -- A generative pre-trained transformer for cross-domain entity  trajectory forecasting

Advances in perception modeling have significantly improved the performance
of object tracking. However, the current methods for specifying the target
object in the initial frame are either by 1) using a box or mask template, or
by 2) providing an explicit language description. These manners are cumbersome
and do not allow the tracker to have self-reasoning ability. Therefore, this
work proposes a new tracking task -- Instruction Tracking, which involves
providing implicit tracking instructions that require the trackers to perform
tracking automatically in video frames. To achieve this, we investigate the
integration of knowledge and reasoning capabilities from a Large
Vision-Language Model (LVLM) for object tracking. Specifically, we propose a
tracker called TrackGPT, which is capable of performing complex reasoning-based
tracking. TrackGPT first uses LVLM to understand tracking instructions and
condense the cues of what target to track into referring embeddings. The
perception component then generates the tracking results based on the
embeddings. To evaluate the performance of TrackGPT, we construct an
instruction tracking benchmark called InsTrack, which contains over one
thousand instruction-video pairs for instruction tuning and evaluation.
Experiments show that TrackGPT achieves competitive performance on referring
video object segmentation benchmarks, such as getting a new state-of the-art
performance of 66.5 $\mathcal{J}\&\mathcal{F}$ on Refer-DAVIS. It also
demonstrates a superior performance of instruction tracking under new
evaluation protocols. The code and models are available at
\href{https://github.com/jiawen-zhu/TrackGPT}{this https URL}.

该研究论文介绍了一种名为 TrackGPT 的追踪器，它采用来自大型视觉语言模型（LVLM）的知识和推理能力进行复杂的基于推理的目标追踪，通过提供隐式的追踪指令实现自动追踪，其在指示追踪基准测试中表现出竞争性的性能。