Auto understanding of human activities in video is an increasing necessity in some application realms. The existing methods for human’s activity identification are divided into two methods: activity recognition and activity detection. The most important challenge in activity detection realm is activity boundary false detection which decreases system accuracy. In this research, an activity detection system was suggested denoting rapid interference and sewing it. Although it has improved accuracy it has also accuracy time, activities in suggested system were replayed more usefully and influenced by creating a descriptor denoting movable and apparent form. The suggested system was tested on Weizmann dataset and reached an accuracy of 93.34%. Furthermore, the proposed system in activity recognition was tested on KTH dataset and reached an accuracy of 93.63%. When activity recognition is stated as a learning case, sufficient labeled educational examples must be used. But labeling the video data is expensive, so the useful method uses unlabeled and labeled examples, during the learning process, this idea is the basic foundation of the semi-supervised method. In this research, a semi-supervised method with co-training algorithm appearance and active learning was suggested which improved the efficiency of semi-supervised learning that was tested.

在某些应用领域中，对视频中人类活动的自动理解已变得越来越必要。现有的人类活动识别方法分为活动识别和活动检测两种方法。活动检测领域中最重要的挑战是活动边界错误检测，这会降低系统准确性。在这项研究中，建议一种活动检测系统表示快速干扰并缝制它。尽管它提高了准确性，但它也具有准确性时间，但是建议的系统中的活动被更有用地重放，并受到创建表示可移动和明显形式的描述符的影响。所建议的系统在Weizmann数据集上进行了测试，达到了93.34％的准确性。此外，在KTH数据集上对所提出的活动识别系统进行了测试，准确率达到93.63％。当将活动认可作为学习案例时，必须使用足够的带有标签的教育示例。但是标记视频数据是昂贵的，因此有用的方法使用未标记和标记的示例，在学习过程中，此思想是半监督方法的基本基础。在这项研究中，提出了一种具有联合训练算法外观和主动学习的半监督方法，该方法提高了被测试的半监督学习的效率。这个想法是半监督方法的基础。在这项研究中，提出了一种具有联合训练算法外观和主动学习的半监督方法，该方法提高了被测试的半监督学习的效率。这个想法是半监督方法的基础。在这项研究中，提出了一种具有联合训练算法外观和主动学习的半监督方法，该方法提高了被测试的半监督学习的效率。