中文摘要：

为了识别公共区域等特定场所下的人体行为,提出了一种基于深度学习的人体行为识别方法。首先,预处理训练样本集和测试样本集中的所有图像,通过高斯混合模型提取出目标运动前景。其次,对训练样本集中各种目标行为建立样本库,定义不同类别的识别行为作为先验知识,用于训练深度学习网络。最后,结合深度学习所得到的网络模型,分类识别测试样本集中的各种行为,并将识别的结果和当前流行方法进行了比较。实验结果表明,该人体行为识别方法优于其它方法,平均识别率相比其他方法有较大的提高。

英文摘要：

To recognize human behaviors in public areas,a new method of recognition was proposed based on deep learning.First,we pre-processed all the images in training and test samples,and utilized GMM to extract moving objects.Then,we built sample sets of various behaviors,and defined different behaviors as priori knowledge to train a deep learning network.Finally,all kinds of behaviors based on the network model of deep learning were recognized.Experimental results demonstrated our method outperforms the existed methods,and the average recognition rate is 96.82%.