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发明公开
CN105678216A 基于深度学习的时空数据流视频行为识别方法 无效 - 驳回

基于深度学习的时空数据流视频行为识别方法
摘要：
本发明提出了一种基于深度学习的时空数据流视频行为识别方法。相比于传统使用单数据流，也就是单一视频流的深度学习方法，本发明使用了时空数据流，即空间流和时间流，空间流从静态的视频帧中识别视频中的目标类别，而时间流从视频中的运动成分中识别目标的运动，最后将这两者的分类结果相融合，得到最终的行为类别。本发明将目标和运动分开识别，能够减少神经网络计算的负担，同时有效提高准确率。
摘要（英）：

The invention proposes a spatio-temporal data stream video behavior recognition method based on deep learning. Compared with a traditional deep learning method which utilizes a signal data stream, namely a single video stream, the spatio-temporal data stream video behavior recognition method based on deep learning utilizes spatio-temporal data streams, that is a spatial stream and a temporal stream, the temporal stream recognizes category of a target in a video from a static video frame, the spatial stream recognizes motion of the target from motion components in the video, and classification results of the temporal stream and the spatial stream are integrated to obtain a final behavior category. The spatio-temporal data stream video behavior recognition method based on deep learning recognizes the target and the motion separately, can reduce calculation burden of a neural network, and effectively increases accuracy.

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