  Most existing multi-object tracking methods typically learn visual tracking
features via maximizing dis-similarities of different instances and minimizing
similarities of the same instance. While such a feature learning scheme
achieves promising performance, learning discriminative features solely based
on visual information is challenging especially in case of environmental
interference such as occlusion, blur and domain variance. In this work, we
argue that multi-modal language-driven features provide complementary
information to classical visual features, thereby aiding in improving the
robustness to such environmental interference. To this end, we propose a new
multi-object tracking framework, named LG-MOT, that explicitly leverages
language information at different levels of granularity (scene-and
instance-level) and combines it with standard visual features to obtain
discriminative representations. To develop LG-MOT, we annotate existing MOT
datasets with scene-and instance-level language descriptions. We then encode
both instance-and scene-level language information into high-dimensional
embeddings, which are utilized to guide the visual features during training. At
inference, our LG-MOT uses the standard visual features without relying on
annotated language descriptions. Extensive experiments on three benchmarks,
MOT17, DanceTrack and SportsMOT, reveal the merits of the proposed
contributions leading to state-of-the-art performance. On the DanceTrack test
set, our LG-MOT achieves an absolute gain of 2.2\% in terms of target object
association (IDF1 score), compared to the baseline using only visual features.
Further, our LG-MOT exhibits strong cross-domain generalizability. The dataset
and code will be available at ~\url{https://github.com/WesLee88524/LG-MOT}.
