# Multiple Pedestrian Tracking in Dense Crowds Combined with Head Tracking
## 1 Introduction  
Multiple-object tracking (MOT) is a kind of general algorithm that can be applied in various fields of computer vision, such as video surveillance, autonomous driving, human-computer interaction, and the medical field. In these scenarios, we can use MOT algorithms to compute the positions, shapes, speeds, trajectories and other information of targets in tracked videos, and further accomplish the functions of object behaviour analysis or object counting. In addition, the reliable motion tracklets generated by MOT algorithms could also effectively compensate for the missed detections in object-detection tasks, and help the detectors to perform more accurately. In the real MOT tasks in dense crowds, occlusions among pedestrians are always very difficult for trackers. This phenomenon is manifested when the view of some pedestrians is completely or partially covered by other pedestrians who are closer to the camera. Occlusions make it difficult to perceive pedestrians' visual clues, i.e., the information of targets is lost. The key to the tracking algorithm is to gather enough target information to determine where the targets are, and assign a unique ID for each target. Therefore, occlusions expose a great number of challenges to the reliability for pedestrian tracking, which may lead to unstable tracklets and even loss of targets. These phenomena cause the rise of some metrics, such as mostly lose (ML), false negative (FN), false positive (FP), identity switch (IDSw), and other indicators for MOT, and the decline of indicators, such as multiple object-tracking accuracy (MOTA), ID F1 score (IDF1), and higher-order tracking accuracy (HOTA). To the researchers of MOT algorithms, this is not what they expect to see. Compared with the general MOT scenes, the huge number of targets in the dense crowd leads to more serious mutual occlusions among the targets, because the frequency and coverage of occlusions in the dense crowd become more critical. This phenomenon ismanifested in the real video recordings that the target A in the video blocks another target B, and the target B blocks the target C at the same time. These layer-by-layer occlusions cause the relationship among the targets to become more chaotic, and bring more instability for the MOT task. The way to effectively handle occlusions, especially severe and frequent occlusions in dense crowds, has always been a difficult issue for MOT tasks in crowds. At present, most MOT systems cannot deal with serious occlusion problems, nor can they provide criteria for judging when to terminate the unconfirmed tracklets and when to restart the killed tracklets of targets, and there is no corresponding guidance method to reobtain the targets when they are lost. In conventional MOT algorithms, rather than any single part of the target, researchers directly select the entire target as the object to be tracked. Those methods, as the current mainstream research methods [1; 2; 3; 4; 5], had indeed achieved considerable results. However, when conducting multiobject research in dense crowds, the effectiveness of those methods will be greatly reduced. As mentioned above, the relationships among the targets in dense crowds are extremely chaotic. One target is likely to block several other targets, causing the motion and appearance features of these targets to be lost in overwhelming quantities. The trackers cannot capture enough valid information, which leads to a significant drop in their performance. The head is the highest and least occluded part of a pedestrian. It is reflected that in dense crowd scenes, the detector for heads can detect a large number of heads, but the detector for full bodies cannot. As shown in Figure 1, in this picture, the head detector has successfully located and recognized the majority of heads, but the full body detector fails.