# An Effectively Finite-Tailed Updating for Multiple Object Tracking in Crowd Scenes
## 2 Related Work  
### Multi-Object Tracking Framework  
MHT [10] is essentially an extension of the Kalman filter [11] method in MOT tasks. It performs trajectory association by solving the conditional probability model of the association between the existing trajectory and the current detection. NOMT [12] designed an accurate affinity measure to correlate detections and achieve efficient and accurate (approximate) online MOT tracking. With the rapid development of deep learning, the detector of a MOT algorithm begins to use deep neural networks and apply CNN-based object detectors, such as Faster R-CNN [13] and YOLOv3 [14], to localize all objects of interest in the input frame. The algorithm is divided into Detection-Based Tracking (DBT) and Joint Detection Tracking (JDT) methods.  
Detection-Based Tracking methods treat object detection and Re-ID as two separate tasks. The MHT-DAM [15] algorithm is an extension of multiple hypotheses tracking (MHT), which incorporates online discriminative appearance modeling, but it cannot be tracked online and the algorithm runs slowly. DeepSort [16] preliminarily realized the balance of accuracy, speed, and ID switches (IDs) of the algorithm, but compared with the latest algorithm, it has a big gap in the index of accuracy and object IDs. The TubeTK [17] algorithm adopted tubes to track multiple objects. This alogorithm uses multiple frames  
Figure 1: Illustration of the object misidentification problem caused by occlusions. The numbers above the pictures represent the frame numbers of the pictures. The red arrow points to the object we are tracking. The number in each object box represents the ID we assigned to the object.  
of information at the same time, its spatio-temporal features are highly coupled, and it performs well in the case of low visibility and occlusion, but the algorithm is not an end-to-end network in the strict sense, and the association part is still an algorithm independent of the network and is based on artificial rules. [PERSON] [18] proposed an improved YOLV3 algorithm to address the problem of low detection accuracy resulting from an exceptionally unbalanced sample distribution of different traffic signs in the TT100K, but it also is not an end-to-end network, and it is not elegant enough. [PERSON] [19] proposed a novel detection framework using multi-object tracking for vehicle-mounted FIR pedestrian detection, but it uses multiple KCF trackers, and cannot achieve real-time tracking.  
Joint Detection Tracking methods simultaneously accomplish object detection and identity embedding (Re-ID features) in a single network to reduce the inference time by sharing most of the computation. It is divided into two branches. One is the anchor-based algorithm, Tracktor++ [20], which uses the existing depth-detection algorithm to integrate the tracking function into the detection module to simplify the training on the tracking task. Its advantage is its use of a detection network to realize the tracking function, which greatly improves the accuracy and speed of the algorithm, but it relies too much on detectors. JDE [21] used a network to realize the joint learning of detecting and embedding apparent features. It can run in real time and has good accuracy. However, the presence of the anchor frame leads to a misalignment of the detection and the embedding of the apparent features. The other is the anchor-free algorithm, which offers a larger and more flexible solution space. It eliminates the computational burden associated with the use of anchors, making detection and segmentation tasks faster and more accurate. [PERSON] [22] proposed a Hierarchical Deep High-Resolution Network (HDHNet) and an end-to-end framework for the feature extraction of small targets in UAV scenes, but it does not manage real-time tracking. CenterTrack [23] ran in real time without relying on appearance characteristics, achieving high tracking accuracy, but it has many IDs for long-time tracking.