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One of the recent trends in vision problems is to use natural languagecaptions to describe the objects of interest. This approach can overcome somelimitations of traditional methods that rely on bounding boxes or categoryannotations. This paper introduces a novel paradigm for Multiple ObjectTracking called Type-to-Track, which allows users to track objects in videos bytyping natural language descriptions. We present a new dataset for thatGrounded Multiple Object Tracking task, called GroOT, that contains videos withvarious types of objects and their corresponding textual captions describingtheir appearance and action in detail. Additionally, we introduce two newevaluation protocols and formulate evaluation metrics specifically for thistask. We develop a new efficient method that models a transformer-basedeMbed-ENcoDE-extRact framework (MENDER) using the third-order tensordecomposition. The experiments in five scenarios show that our MENDER approachoutperforms another two-stage design in terms of accuracy and efficiency, up to14.7% accuracy and 4$\times$ speed faster.

Type-to-Track: Retrieve Any Object via Prompt-based Tracking