The existing pedestrian multi-object tracking algorithms have the problems of undetectable pedestrians and inter-frame association confusion in dense scenes. In order to improve the precision of pedestrian tracking in dense scenes， a head tracking model based on full-body appearance features was proposed， namely HT-FF （Head Tracking with Full-body Features）. Firstly， the head detector was used to replace the full-body detector to improve the detection rate of pedestrians in dense scenes. Secondly， using the information of human posture estimation as a guide， the noise-removed full-body appearance features were obtained as tracking clues， which greatly reduced the confusion in the association among multiple frames. HT-FF model achieves the best results on multiple indicators such as MOTA （Multiple Object Tracking Accuracy） and IDF1 （ID F1 Score） on benchmark dataset of pedestrian tracking in dense scenes — Head Tracking 21 （HT21）. The HT-FF model can effectively alleviate the problem of lost and confused pedestrian tracking in dense scenes， and the proposed tracking model combining multiple clues is a new paradigm of pedestrian tracking model.