# A Review of Deep Learning-Based Visual Multi-Object Tracking Algorithms for Autonomous Driving
## 4 TBD Algorithms Based Deep Learning
### TBD Algorithm Based on Deep Learning Object Tracking
#### 4.2.1 SORT-Based Object Tracking  
The SORT [50] object tracking algorithm is an online, real-time, multi-object tracking algorithm that incorporates correlation filters into a deep learning algorithm. It predicts the current position using a Kalman filter, correlates the detection frames and objects by correlation, and uses the Intersection over Union (IoU) between each detection and all predicted bounding boxes of an existing object as a metric for the object relationship between the preceding and following frames. Although the object tracking method for SORT is quick, it scarcely addresses object occlusion, leading to a large number of ID switches; accuracy is good in the absence of occlusion but low in the presence of occlusion.  
Wojke et al. [51] later proposed the DeepSORT object tracking algorithm in 2017, which extracts the object's apparent features for cascade matching, improves object tracking in the presence of occlusion, and also lessens the issue of object ID switches, to make up for these shortcomings of the SORT algorithm. The main idea behind this approach is to combine frame-by-frame data association, recursive Kalman filtering, and a conventional single-hypothesis tracking method. In order to enhance the ranking algorithm's efficiency and reduce the frequency of ID switches, DeepSORT adds a pre-trained appearance vector based on ResNet [52] network extraction and embeds the cosine distance between features as a cost matrix into the SORT algorithm. Because of this, even if the object is obscured and subsequently reappears, it can still successfully match the ID. Many other researchers adopted comparable CNN networks including GoogleNet [53], ResNet, and InceptionNet [54], as did Mahmoudi et al. [55], Fang et al. [56], Sheng et al. [57], and Chen et al. [48]. By altering the task-related training data, enhancing the associated loss function, learning to identify apparent features that can differentiate between similar objects, and attempting to extract more robust apparent features, the feature extraction backbone network's depth can be increased, but this does not yield much. Lin et al. [58] contend that the TBD algorithm heavily relies on appearance information. As a result, they proposed the hybrid track association (HTA) algorithm, which makes use of the appearance distance in the previous tracking track frame and, by using incremental high number mixture model (IGMM) modeling and incorporating the statistical data derived therefrom, significantly enhances the object recognition. Du et al. [59] optimized DeepSORT in terms of detection, embedding, and association, and proposed the StrongSORT algorithm, which embeds two more lightweight and plug-and-play algorithms, one is an appearance-free link model (AFLink), which associates short trajectories into complete trajectories, the second is to use Gaussian-smoothed interpolation (GSI) to compensate for missed detections and achieve higher accuracy object localization. The StrongSORT algorithm still has several limitations. The main concern is their relatively low running speed compared with joint trackers and several appearance-free separate trackers. Further research on improving computational efficiency is necessary.