Zero-Shot Open-Vocabulary Tracking with Large Pre-Trained Models

Object tracking is central to robot perception and scene understanding, allowing robots to parse a video stream in terms of moving objects with names. Tracking-by-detection has long been a dominant paradigm for object tracking of specific object categories [1], [2]. Recently, large-scale pre-trained models have shown promising advances in detecting and segmenting objects and parts in 2D static images in the wild. This raises the question: can we re-purpose these large-scale pre-trained static image models for open-vocabulary video tracking? In this paper, we combine an open-vocabulary detector [3], segmenter [4], and dense optical flow estimator [5], into a model that tracks and segments any object in 2D videos. Given a monocular video input, our method predicts object and part mask tracks with associated language descriptions, rebuilding the pipeline of Tractor [6] with modern large pre-trained models for static image detection and segmentation: we detect open-vocabulary object instances and propagate their boxes from frame to frame using a flow-based motion model, refine the propagated boxes with the box regression module of the visual detector, and prompt an open-world segmenter with the refined box to segment the objects. We decide the termination of an object track based on the objectness score of the propagated boxes as well as forward-backward optical flow consistency. We re-identify objects across occlusions using deep feature matching. We show that our model achieves strong performance on multiple established benchmarks [7], [8], [9], [10], and can produce reasonable tracks in manipulation data [11]. In particular, our model outperforms previous state-of-the-art in UVO and BURST, benchmarks for open-world object tracking and segmentation, despite never being explicitly trained for tracking. We hope that our approach can serve as a simple and extensible framework for future research and enable imitation learning from videos with unconventional objects.