Current multi-object tracking (MOT) aims to predict trajectories of targets
(i.e.,"where") in videos. Yet, knowing merely "where" is insufficient in many
crucial applications. In comparison, semantic understanding such as
fine-grained behaviors, interactions, and overall summarized captions (i.e.,
"what") from videos, associated with "where", is highly-desired for
comprehensive video analysis. Thus motivated, we introduce Semantic
Multi-Object Tracking (SMOT), that aims to estimate object trajectories and
meanwhile understand semantic details of associated trajectories including
instance captions, instance interactions, and overall video captions,
integrating "where" and "what" for tracking. In order to foster the exploration
of SMOT, we propose BenSMOT, a large-scale Benchmark for Semantic MOT.
Specifically, BenSMOT comprises 3,292 videos with 151K frames, covering various
scenarios for semantic tracking of humans. BenSMOT provides annotations for the
trajectories of targets, along with associated instance captions in natural
language, instance interactions, and overall caption for each video sequence.
To our best knowledge, BenSMOT is the first publicly available benchmark for
SMOT. Besides, to encourage future research, we present a novel tracker named
SMOTer, which is specially designed and end-to-end trained for SMOT, showing
promising performance. By releasing BenSMOT, we expect to go beyond
conventional MOT by predicting "where" and "what" for SMOT, opening up a new
direction in tracking for video understanding. Our BenSMOT and SMOTer will be
released.

引入了 Semantic Multi-Object Tracking (SMOT) 的研究，旨在估计物体的轨迹并理解与轨迹相关的语义细节，包括实例描述、实例交互和整体视频描述，整合了 “where” 和 “what” 以进行跟踪。同时介绍了用于 SMOT 的一个大规模基准测试集 BenSMOT，提供了目标轨迹的注释以及自然语言的实例描述、实例交互和整体描述。此外还提出了专门为 SMOT 设计和端到端训练的新型跟踪器 SMOTer，并通过发布 BenSMOT 和 SMOTer 来推动跟踪领域朝着了解视频的新方向发展。