Department of Mathematics, Jashore University of Science and Technology, Jashore 7408, Bangladesh.
Department of Electrical and Computer Engineering, Abbottabad Campus, COMSATS University Islamabad, Abbottabad 22060, Pakistan.
Sensors (Basel). 2021 May 24;21(11):3642. doi: 10.3390/s21113642.
This paper proposes an action recognition framework for depth map sequences using the 3D Space-Time Auto-Correlation of Gradients (STACOG) algorithm. First, each depth map sequence is split into two sets of sub-sequences of two different frame lengths individually. Second, a number of Depth Motion Maps (DMMs) sequences from every set are generated and are fed into STACOG to find an auto-correlation feature vector. For two distinct sets of sub-sequences, two auto-correlation feature vectors are obtained and applied gradually to L2-regularized Collaborative Representation Classifier (L2-CRC) for computing a pair of sets of residual values. Next, the Logarithmic Opinion Pool (LOGP) rule is used to combine the two different outcomes of L2-CRC and to allocate an action label of the depth map sequence. Finally, our proposed framework is evaluated on three benchmark datasets named MSR-action 3D dataset, DHA dataset, and UTD-MHAD dataset. We compare the experimental results of our proposed framework with state-of-the-art approaches to prove the effectiveness of the proposed framework. The computational efficiency of the framework is also analyzed for all the datasets to check whether it is suitable for real-time operation or not.
本文提出了一种使用 3D 时空梯度自相关（STACOG）算法的深度图序列动作识别框架。首先，将每个深度图序列分别拆分为两个不同帧长的子序列集。其次，从每个集合中生成多个深度运动图（DMM）序列，并将其输入 STACOG 以找到自相关特征向量。对于两个不同的子序列集，获得两个自相关特征向量，并逐步应用于 L2-正则化协同表示分类器（L2-CRC）以计算一对残差值。接下来，使用对数意见池（LOGP）规则组合 L2-CRC 的两种不同结果，并为深度图序列分配一个动作标签。最后，在三个名为 MSR-action 3D 数据集、DHA 数据集和 UTD-MHAD 数据集的基准数据集上评估我们提出的框架。我们将我们提出的框架的实验结果与最先进的方法进行比较，以证明该框架的有效性。还分析了该框架在所有数据集上的计算效率，以检查其是否适合实时操作。
