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Title: A New Solution to Skeleton-Based Human Action Recognition via the combination usage of explainable feature extraction and sparse sampling global features.

Abstract:?With the development of deep learning technology, the vision-based applications of human action recognition (HAR) have received great progress. Many methods followed the idea of data-driven and tried their best to include more and more motion features in consideration for higher accuracy purposes. However, the thought of “the more features adopted, the higher accuracy will be”will inevitably result in the ever-increasing requirement of computing power and decreasing efficiency. In this paper, in order to effectively recognize human actions with only a few of the most sensitive motion features, the explainable features, the combining usage of local and global features, and a multi-scale shallow network are proposed. First, the explainable features let a deep neural network be finetuned in the input stage, and an action represented by these features are easier to find priori theory of physics and kinematics for data augmentation purpose. Second, although criticism of the global features never stops, it is universally acknowledged that the context information included in the global feature is essential to HAR. The proposed SMHI—motion history image generated in a sparse sampling way, can not only reduce the time-cost, but also effectively reflect the motion tendency. It is suggested to be a useful complementary of local features. Third, full experiments were conducted to find out the best feature combination for HAR. The results have proved that feature selection is more important than computing all features. The proposed method is evaluated on three datasets. The experiment results proved the effectiveness and efficiency of our proposed method. Moreover, the only usage of human skeleton motion data provides privacy assurances to users.

現(xiàn)在大多數(shù)方法有兩個問題：1. 將盡可能多的特征納入到輸入端，雖然可以增強準確率，但增加了計算負擔，而且模型越來越臃腫；2. 全局特征一直處于被拋棄的境地，而其包含的上下文信息卻有非常重要。針對這兩點，我嘗試用物理學和運動學中的先驗知識提取人體行為動作特征，使其具備可解釋性，然后對其優(yōu)化和數(shù)據(jù)增強。并進一步找到其最有效的組合。同時，通過稀疏采樣的方式構建MHI，即：只提取其運動趨勢特征。使之作為local feature的有效補充。實驗結果良好，特別是在效率方面有質的提升。本文的主要創(chuàng)新點在于跳出了主流“數(shù)據(jù)驅動”特征越多越好的傳統(tǒng)思路，通過實驗證明：特征選擇遠比計算所有特征更為重要。

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