Action Recognition with Non-Uniform Key Frame Selector

Haohe Li, Chong Wang, Shenghao Yu, Chenchen Tao
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Abstract

Current approaches for spatiotemporal action recognition have achieved impressive progress, especially in temporal information processing. Meanwhile, the power of spatial information may be underestimated. Thus, a non-uniform key frame selector is proposed to pick the most representative frames according to the relationship between frames along the temporal dimension. Specifically, the reweight high-level frame features are used to generate an importance score sequence, while the key frames, in each temporal section, are selected based on the above scores. Such selected frames have richer semantic information, which has positive impact on the network training. The proposed model is evaluated on two action recognition, namely datasets HMDB51 and UCF101, and promising accuracy improvement is achieved.
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非统一关键帧选择器的动作识别
目前的时空动作识别方法已经取得了令人瞩目的进展,特别是在时间信息处理方面。同时,空间信息的力量可能被低估了。在此基础上,提出了一种非均匀关键帧选择器,根据帧与帧之间沿时间维的关系选取最具代表性的帧。具体来说,使用重权高级帧特征来生成一个重要分数序列,而每个时间段中的关键帧则基于上述分数进行选择。这样选择的帧具有更丰富的语义信息,对网络训练有积极的影响。在两个动作识别数据集HMDB51和UCF101上对该模型进行了评估,结果表明该模型的准确率有了较大的提高。
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