# FusionTrack: Multiple Object Tracking with Enhanced Information Utilization
## 2 Related Works
### Two-Stage Track-by-Attention Methods  
Two-stage track-by-attention methods primarily follow the track-by-detection (TBD) design [4; 5; 6; 24; 25; 26; 27], a dominant multiple-object tracking paradigm. These methods can be further divided into four main parts according to [26] detection, motion model, association and track life cycle management. At each frame, a detection model is used to localize and classify objects. Simultaneously, a motion model is employed to predict the locations of tracked objects in the current frame. The detection and track predictions are then associated based on their similarity. Finally, track states are updated and new tracks are added, following hand-crafted life cycle management rules.  
First proposed by [PERSON] [6], the track-by-detection paradigm achieves competitive results despite its simple design. It uses only a Kalman filter as a motion model, intersection over union (IoU) as a similarity metric, and the Hungarian algorithm as an association method on top of an existing detector. DeepSORT [24] and Tracktor [25] improved the association component by introducing the cosine distance of appearance features as a matching cost. Sim pleTrack [26] formulated the four components of the track-by-detection method and selected the best methods for each component based on extensive experimentation. ByteTrack [4] introduced a two-stage association method to better match severely occluded objects with low confidence. BOT-SORT [5] introduced camera motion compensation and modified the track state representation in the Kalman filter to achieve state-of-the-art performance.  
One characteristic of track-by-detection methods is that their four components are all replaceable. State-of-the-art detectors can be directly integrated into the tracker, allowing tracking performance to benefit from detectors pre-trained on large-scale datasets. In this work, we adopt the separable detector design to unleash the full potential of pre-trained detectors.  
Inspired by transformer-based detection methods, TransTrack [9] introduced the decoder architecture into the field of multiple-object tracking to refine track positions from the previous frame. Object queries and track queries are processed with different decoders and then matched using the Kuhn-Munkres algorithm applied to IoU similarity. Matched detection features are used as track queries for the next frame, and unmatched detections are filtered to create new tracklets. Following TransTrack, TransCenter [10] introduced a dense pixel-level multi-scale representation for detection prediction. These methods use separate decoders to object and track queries and establish associations using traditional matching algorithms as the track-by-detection methods used. The tracking branch lacks information from detection objects and therefore suffers from low accuracy.  
In contrast to these methods, we adopt a masked self-attention mechanism to allow information to flow from the detection branch to the tracking branch. In this way, the tracking branch can utilize pre-trained knowledge from the detection branch and achieve better performance.