视频监控中出入口人数统计的难点在于人流密集时对每个人体的准确分割。通过学习的方法得到人头检测的分类器，并在垂直拍摄图像中提取人头候选区域，以分离相互靠近的人体目标，进而根据人头的运动特征剔除静止误检区域，根据误检目标检测频率低及其响应位置不连续的特征剔除动态误检区域。最后提出一种简易可行的过线跟踪方案以完成计数。实验中对各种复杂情况的过线视频进行测试，正确率能够达到95%以上。与传统方法相比，本文方法解决了多人过线或搬货物过线时传统方法难以准确完成的行人分割和计数，更适于实际情况的人数统计。

The difficulty of counting the number of people using video surveillance lies in the correct segmentation of each person in crowded situations.For this problem，we train a classifier to detect head candidates so as to distinguish between people walking closely under the overhead mounted camera.The static false detected candidates are removed according to the motion feature of the head，while the dynamic ones are eliminated due to their low detected frequency and discontinuous response positions.Finally，an easy and effective crossing tracking scheme is proposed for counting.We conducted experiments in various complex situations.It shows that the counting rate is over 95%.Compared to the traditional method，the proposed means can segment and count the people more accurately in cases where multiple people are crossing or crossing with items.It is therefore much more suitable for people counting in real life situations.