Datalevel information enhancement: Motionpatchbased Siamese Convolutional Neural Networks for human activity recognition in videos Abstract   Data augmentation is critical for deep learningbased human activity recognition (HAR) systems.xc2xa0However, conventional data augmentation methods, such as randomcropping, may generate bad samples that are unrelated to a particular activity (e.g. the background patches without saliency motion information).xc2xa0As a result, the randomcropping based data augmentation may affect negatively the overall performance of HAR systems.xc2xa0Humans, in turn, tend to pay more attention to motion information when recognizing activities.xc2xa0In this work, we attempt to enhance the motion information in HAR systems and mitigate the influence of bad samples through a Siamese architecture, termed as Motionpatchbased Siamese Convolutional Neural Network (MSCNN).xc2xa0The term motion patch is defined as a specific square region that includes critical motion information in the video.xc2xa0We propose a simple yet effective method for selecting those regions. To evaluate the proposed MSCNN, we conduct a number of experiments on the popular datasets UCF101 and HMDB51.xc2xa0The mathematical model and experimental results show that the proposed architecture is capable of enhancing the motion information and achieves comparable performance.

Augmenting SpatioTemporal Human Motion Data for Effective 3D Action Recognition Action recognition is a fundamental operation in 3D human motion analysis. Existing deep learning classifiers achieve a high recognition accuracy if large amounts of training data are provided. However, such data are difficult to obtain in a variety of application scenarios, mainly due to the high costs of motion capture technologies and an absence of suitable actors. In this paper, we propose augmentation techniques to artificially enlarge existing collections of 3D human skeleton sequences. The proposed techniques are especially useful for datasets distinguishing in a high number of classes, each of them characterized by only a limited number of action samples. We experimentally demonstrate that the augmented data help to significantly increase the recognition accuracy even using a standard deep learning architecture.