The majority of target tracking algorithms assume that the probability of the target detection is independent of track state,but it is usually incorrect with the performance degradation of the target tracking. For this issue,a target moving trail tracking algorithm based on state detection is proposed. Unlike most current algorithms,the proposed algorithm is unfolded based on the correlation of target moving trail and detection state. Each tracked probability density function from previous mo⁃ment to current moment is forecasted,and then a subset for update is selected from the measured value set and the similarity measurement is calculated. The tracking quality is iteratively calculated,the target trail is finally evaluated,and the state depen⁃dence probability of target detection is fused into multi⁃target tracker. The simulation test was performed by means of the two⁃di⁃mensional multi⁃target tracking. The alternate fixed sensors are used to scan the monitored area per second. The scanning results show that the tracking identification rate can reach up to 97%,and the tracking performance is significantly improved. In addi⁃tion,the algorithm has a good tracking effect in other traffic videos,and has good practicability.%目前大多数的目标跟踪算法都假设目标检测的概率与轨迹的状态无关，但是随着目标跟踪性能的降低，这个假设通常不正确，针对此问题，提出一种基于状态检测的目标运动轨迹跟踪算法。不同于大多数现有算法，该算法在目标运动轨迹与检测状态相关的基础上展开。首先预测从先前时刻到当前时刻的每个跟踪的概率密度函数；然后从测量值集合中选取一个子集用于更新，并计算相似度测量；最后迭代计算跟踪质量和目标轨迹评估，将目标检测的状态依赖概率融入到多目标跟踪器中。利用二维多目标跟踪进行仿真实验，交替固定传感器每秒都对监控区域进行扫描，结果显示，跟踪确认率可达97%，显著提高了跟踪性能。此外，该算法在其他交通视频上也有较好的跟踪效果，具有较好的实用性。
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