Abstract： To solve the problem that probability hypothesis density (PHD) filter was not able to track birth targets of unknown positions, a PHD filtering framework was proposed based on new target detection. To overcome the inability to yield target tracks of original PHD filter, a ″trackstate estimate″ association algorithm was designed to present mathematical formulation and implementation method for track recognition. The implementation of the multitarget tracking framework for a varying number of targets was proposed. The proposed algorithm was realized by MATLAB, and two pedestrian surveillance data sets with new targets and occlusion were adopted to evaluate the performance. The results show that the proposed tracker can improve the response of PHD filter to new births and targets after occlusion by updating the intensity of new birth targets in terms of position observations. The birth targets of unknown positions can be tracked in the scenario at any time with good accuracy of target number and state estimation.
吴静静, 尤丽华, 安伟, 宋淑娟, 周德强. 基于概率假设密度的目标数变化视频跟踪算法[J]. 江苏大学学报（自然科学版）, 2015, 36(6): 697-705.
WU Jing-Jing, YOU Li-Hua, AN Wei, SONG Shu-Juan, ZHOU De-Qiang. Tracking a varying number of targets in videos based on
probability hypothesis density filtering[J]. Journal of Jiangsu University(Natural Science Eidtion)
, 2015, 36(6): 697-705.
2015, Vol. 36 
