针对多目标跟踪过程中存在的遮挡问题，提出了一种固定摄像机场景下的多目标实时跟踪算法。提出基于鬼影判别与背景模型选择更新的背景差法检测运动目标，建立一种融合色度与边缘特征的目标模型.通过定义稳定跟踪队列、临时跟踪队列、跟踪丢失队列以及候选跟踪队列等跟踪器队列，提出基于多级关联匹配的策略实现多目标跟踪遮挡处理，针对新目标、目标合并以及目标消失分别提出判别及跟踪策略。实验结果表明,运动目标检测方法能够抑制鬼影,防止缓慢运动的目标融入背景;同时,验证了目标模型的稳健性，以及跟踪算法能够在遮挡、交错等复杂情形下有效地跟踪多目标。

Scene-based adaptive nonuniformity correction (NUC) is currently being applied to achieve higher performance in infrared imaging systems. However, almost all scene-based NUC algorithms cause the production of ghosting artifacts over output images. Based on constant-statistics theory, we propose a novel threshold self-adaptive ghosting reduction algorithm to improve the space low-pass and temporal high-pass (SLPTHP) NUC technique. The correction parameters of the previous frame are regarded as thresholds to compute new correction parameters. Experimental results show that the proposed algorithm can obtain a satisfactory performance in reducing unwanted ghosting artifacts.