# Multi-Object Tracking with Grayscale Spatial-Temporal Features
## 2 Related Work  
Some research on TBD mainly relied on the motion features or appearance features of the target. [PERSON] et al. [4] modeled the position and velocity of each target and then, based on the Intersection-over-Union (IoU), matched the target. Due to the long-term loss of the detected target, the Kalman filter parameters deviated from the true value, and tracking might fail. To solve this problem, [PERSON] et al. [8] proposed OC-SORT, which proposed an observation-centric update strategy to reduce the accumulated errors with time. [PERSON] et al. [9] also aimed to solve the problem of long-term tracking and designed a recovery module to stitch together fragmented trajectories. Unmatched detections and trajectories were matched by calculating the velocity-time relationship between them. [PERSON] et al. [10] designed a feature extraction network based on GoogleNet [11] to extract the appearance features of the target and associated targets through the k-dense neighbor algorithm [12]. However, the tracking capability of these methods, which relied on the motion features, was reduced for scenes with complex motion trajectories, and the appearance model was easily affected by lighting, occlusion, and other conditions, which might cause tracking failure. In order to further improve the tracking accuracy, some methods combined motion and appearance features. [PERSON] et al. [13] designed a self-correcting KF for predicting the target position and evaluated the similarity between targets through a recurrent neural network. [PERSON] et al. [14] combined motion features with appearance. They used a newly introduced strategy of weak cues (confidence state and height state) to increase the robustness for occlusion. However, due to the high complexity of the network and the large number of calculations, the tracking speed of these algorithms is slow, making it difficult to meet the requirements of real-time tracking. Therefore, the paradigm of completing object detection and feature extraction in a single network has attracted attention. [PERSON] et al. [5] proposed the JDE864 algorithm, which integrates object detection and feature extraction into one network. [PERSON] et al. [15] added a branch for extracting instance-level Re-ID features based on RetinaNet [16] for subsequent data association. [PERSON] et al. [17] designed a feature decoupling module and proposed a simple three-stage data association. The combination of detection and re-identification modules in these methods can improve efficiency and reduce the time for feature extraction. But object detection and re-identification are two different tasks, so balancing the differences between the two tasks is a difficult problem, and this paradigm still does not consider optimizing matching. In recent years, the paradigm of completing three subtasks of detection, embedding, and tracking in a single network has attracted the attention of many scholars. Recently, transformer [18] achieved remarkable success in multiple computer vision tasks. Transformer has powerful feature representation capabilities and good parallel computing capabilities. An end-to-end paradigm is proposed based on Transformer. [PERSON] et al. [19] proposed a new MOT framework, TrackFormer. This framework is based on DETR [20] and is an end-to-end framework that uses tracking-by-attention as a new idea to achieve data association between frames. [PERSON] et al. [21] proposed MOTRv2 based on MOTR [22]. They think that it is conflicting for the DETR network to perform the two tasks of detection and data association at the same time. Therefore, they added an additional object detector based on MOTR, and the model is an end-to-end model. Transformer has powerful feature representation capabilities and good parallel computing capabilities. But its computational complexity is high, and its real-time performance is poor. There are some other methods to tracking targets. [PERSON] et al. [23] proposed DOT (dynamic object tracking), which combines instance segmentation and multi-view geometry.