融合多路特征和注意力机制的强化学习关键帧提取算法

针对现有视频关键帧提取算法对运动类视频中运动特征提取不准导致的漏检和误检问题，提出一种融合多路特征和注意力机制的强化学习关键帧提取算法。该算法首先通过人体姿态识别算法对视频序列进行人体骨骼关节点提取；然后使用S-GCN和ResNet50网络分别提取视频序列中的运动特征和静态特征，并将两者进行加权融合；最后应用注意力机制对特征序列进行视频帧重要性计算，并利用强化学习进行关键帧的提取和优化。实验结果表明，该算法能较好地解决运动类视频在关键帧提取中出现的漏误检问题，在检测含有关键性动作的视频帧时表现较好，算法准确率高、稳定性强。

Key frame extraction algorithm of reinforcement learning based on multi-channel feature and attention mechanism

Aiming at the problem of missing detection and false detection caused by inaccurate motion feature extraction of existing video key frame extraction algorithms, this paper proposed a reinforcement learning key frame extraction algorithm combining multi-channel feature and attention mechanism. The algorithm extracted the human skeleton joint points from the video sequence through the human posture recognition algorithm firstly. Then it used the S-GCN and ResNet50 network to extract the motion features and static features in the video sequence respectively, and performed a weighted fusion of the two. Finally it applied the attention mechanism to calculate the importance of the video frame of the feature sequence, and used reinforcement learning to extract and optimize key frames. The experimental results show that the algorithm can solve the problem of missing and false detection in the key frame extraction of motion video. It performs well in the detection of video frames containing key actions, with high accuracy and strong stability.