# Multiple Pedestrian Tracking in Dense Crowds Combined with Head Tracking
## 2 Related Work  
### Tracking by Detection  
_Tracking by detection_ (TDB) is a common paradigm in the MOT field. TDB has the characteristics of high accuracy, relatively fast speed, and real-time performance. It has been the mainstream method in the field of MOT in the past.  
The detection algorithm is the cornerstone of the operation of the TDB paradigm. The addition of the convolutional neural network (CNN) [11] enabled the detection algorithms to achieve rapid development in both detection accuracy and running speed [12; 13; 14; 15; 16; 17; 18; 19]. Powerful detection algorithms could be introduced to TDB trackers to obtain better tracking performance. RetinaNet was a typical object detector essentially composed of CNN plus feature pyramid networks (FPN) [20] plus two fully convolutional networks (FCN) [21]. By using the loss function focal loss, the weights of samples that are difficult to classify were increased, and the ratios of positive and negative samples were effectively controlled. RetinaNet was used by various MOT algorithms, such as RetinaTrack [22] and Chained Tracker [23]. The version of You Only Look Once (YOLO) [24] detectors had gone through from V1 to V5, and the latest YOLO version detectors generally achieved good balance between accuracy and speed, and it was used by a large number of methods [25; 26; 27]. There are many improved versions in the follow-up, and the best performance is You Only Look Once X (YOLOX) [28], which is a very popular detector at present. CenterNet [29] abandoned the widely used anchors in traditional detection algorithms, and derived the center points, widths, heights, offsets, and other information of the targets by introducing heat map, and used the anchor-free method to obtain the bounding boxes of the targets. CenterNet had also been widely used due to its simplicity and efficiency [30; 31; 32; 33; 34]. In general, the vast majority of trackers only implement MOT algorithms through the bounding boxes from the current frames.  
DeepSort [35] is a classic MOT algorithm that complies with the TDB paradigm, and it was improved from the SORT algorithm. In order to predict the trajectories of the targets, researchers usually use state estimation filters, like the Kalman filter [36] and Alpha-Beta filter [37; 38]. Before running DeepSort, it is helpful to use an independent detector like YOLOV3 to detect the targets of interest in each frame of the video, then use the Kalman filter, Hungarian algorithm [39], feature extractor, and other components to comprehensively consider the motion law, appearance similarity, motion similarity, and other information of the target bounding boxes, with the step of Kalman prediction,matching, and Kalman updating to calculate the target motion tracklets. DeepSort runs fast, can meet real-time requirements, and has high accuracy, so it is one of the most widely used MOT algorithms in the industry at present. There are many improved versions of DeepSort, like MOTDT and ByteTrack. ByteTrack is a typical and well-known tracker among the improved works. In contrast to general MOT algorithms that directly discard the bounding boxes with lower confidence, ByteTrack reused the bounding boxes with lower confidence by executing the Kalman filter and Hungarian algorithm twice. The first usage was between bounding boxes with high confidence and tracklets, and the second usage was between bounding boxes with lower confidence and the unmatched tracklets in the first time. It should be noted that the reason for the low confidence of the bounding boxes is general occlusion and indistinctness, and occlusions indicate that the appearance information of those objects is mixed with other bounding boxes, so only the motion law is considered in the second matching, and the appearance information is not considered. Researchers familiar with DeepSort will soon understand ByteTrack.