基于Conv-Involution的红外视频小样本人体行为识别方法北大核心CSCD

红外视频存在颜色信息缺失较为严重、识别区域易与背景混淆等现象,使得现有小样本特征提取网络常常关注无效信息,导致识别精度较低。针对此问题,本文提出一种基于内卷积(Conv-Involution)算子的红外视频小样本人体行为识别方法。首先,通过InstColorization方法恢复红外视频中的颜色信息;其次,基于空间和通道特异性设计Conv-Involution算子,并利用该算子和改进注意力机制设计特征提取网络,分离背景,更有效地关注行为发生区域;最后,结合小样本学习方法ANIL进行人体行为分类。对比实验结果表明,本文所提方法不但识别精度最高,而且具有出色的稳定性。

Few shot human behavior recognition method ofinfrared video based on Conv Involution

Infrared video suffers serious lack of color information,and the recognition area is easily confused with the background,which makes the existing few shot feature extraction network frequently follow invalid data,resulting in low recognition accuracy.To address this problem,few shot human behavior recognition of infrared video based on the Conv Involution operator is proposed.Firstly,the color information in the infrared video is restored by the InstColorization method.Secondly,the Conv Involution operator is designed based on spatial and channel specificity,and the feature extraction network is designed using this operator and an improved attention mechanism to separate the background and focus more effectively on the region where the behavior occurs.Finally,the human behavior is classified by combing the few shot learning method.The comparative experimental results show that the proposed method not only has the highest recognition accuracy,but also has excellent stability.