【摘要】 异常行为检测广泛应用于安防、智能交通、机场监视、监考等领域,但异常行为数据难以获取,算法准确率较低。为了应对上述问题,提出一个基于对抗自编码思想的两路异常检测网络。其中,一路子网络利用像素信息,关注行为发生的整体环境。另一路子网络则利用姿态信息,关注人体行为。然后对两个子网络的结果进行混合,得到异常行为检测的结果。最后,在CUHK Avenue和UCSD Ped数据集上验证结果。更多还原

【Abstract】 Anomaly detection has been widely used in security, intelligent, invigilation, etc. But the abnormal data is difficult to obtain and the accuracies of the current algorithms are not high. To address these problems, proposes a new model, which consisting of two adversarial autoencoders-like(AAE-like) sub-networks. One sub-network focuses on the environment by processing appearance information. Another sub-network focuses on the human behavior by utilizing the human pose information in the frames. Then the results of the two sub-networks are combined to obtain the result. Finally, t evaluates the proposed model on CUHK Avenue and UCSD Ped datasets.更多还原