Human Activity Recognition in RGB-D videos has been an active research topic during the last decade. However, no efforts have been found in the literature, for recognizing human activity in RGB-D videos where several performers are performing simultaneously. In this paper we introduce such a challenging dataset with several performers performing the activities. We present a novel method for recognizing human activities in such videos. The proposed method aims in capturing the motion information of the whole video by producing a dynamic image corresponding to the input video. We use two parallel ResNext-101 to produce the dynamic images for the RGB video and depth video separately. The dynamic images contain only the motion information and hence, the unnecessary background information are eliminated. We send the two dynamic images extracted from the RGB and Depth videos respectively, through a fully connected layer of neural networks. The proposed dynamic image reduces the complexity of the recognition process by extracting a sparse matrix from a video. However, the proposed system maintains the required motion information for recognizing the activity. The proposed method has been tested on the MSR Action 3D dataset and has shown comparable performances with respect to the state-of-the-art. We also apply the proposed method on our own dataset, where the proposed method outperforms the state-of-the-art approaches.
A few techniques have been found in the literature based on deep learning based activity recognition in RGB-D videos @cite_3 @cite_24 . A weighted hierarchical DMM is combined with a 3 layer CNN for feature extraction in @cite_3 . In @cite_3 , different view points are handled by rotating the 3D points of the DMM. In @cite_24 , Wang proposed a deep learning based method where the same CNN is applied on both RGB and Depth frames, and the result feature is passed through a fully connected network, to get a good accuracy. Motivated by the success of Wang and Bilen @cite_9 , we apply ResNext-101 on the dynamic images extracted from both RGB and depth videos, and pass the result vector through a fully connected layer. However, utilizing the dynamic images instead of using the whole video, reduce the computational overhead of the proposed method and provides a comparable accuracy with the state-of-the-arts.
@cite_24 @cite_9 @cite_3
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A novel deep neural network training paradigm that exploits the conjoint information in multiple heterogeneous sources is proposed. Specifically, in a RGB-D based action recognition task, it cooperatively trains a single convolutional neural network (named c-ConvNet) on both RGB visual features and depth features, and deeply aggregates the two kinds of features for action recognition. Differently from the conventional ConvNet that learns the deep separable features for homogeneous modality-based classification with only one softmax loss function, the c-ConvNet enhances the discriminative power of the deeply learned features and weakens the undesired modality discrepancy by jointly optimizing a ranking loss and a softmax loss for both homogeneous and heterogeneous modalities. The ranking loss consists of intra-modality and cross-modality triplet losses, and it reduces both the intra-modality and cross-modality feature variations. Furthermore, the correlations between RGB and depth data are embedded in the c-ConvNet, and can be retrieved by either of the modalities and contribute to the recognition in the case even only one of the modalities is available. The proposed method was extensively evaluated on two large RGB-D action recognition datasets, ChaLearn LAP IsoGD and NTU RGB+D datasets, and one small dataset, SYSU 3D HOI, and achieved state-of-the-art results. We introduce the concept of "dynamic image", a novel compact representation of videos useful for video analysis, particularly in combination with convolutional neural networks (CNNs). A dynamic image encodes temporal data such as RGB or optical flow videos by using the concept of rank pooling'. The idea is to learn a ranking machine that captures the temporal evolution of the data and to use the parameters of the latter as a representation. When a linear ranking machine is used, the resulting representation is in the form of an image, which we call dynamic because it summarizes the video dynamics in addition of appearance. This is a powerful idea because it allows to convert any video to an image so that existing CNN models pre-trained for the analysis of still images can be immediately extended to videos. We also present an efficient and effective approximate rank pooling operator, accelerating standard rank pooling algorithms by orders of magnitude, and formulate that as a CNN layer. This new layer allows generalizing dynamic images to dynamic feature maps. We demonstrate the power of the new representations on standard benchmarks in action recognition achieving state-of-the-art performance. This paper proposes a new method, i.e., weighted hierarchical depth motion maps (WHDMM) + three-channel deep convolutional neural networks (3ConvNets), for human action recognition from depth maps on small training datasets. Three strategies are developed to leverage the capability of ConvNets in mining discriminative features for recognition. First, different viewpoints are mimicked by rotating the 3-D points of the captured depth maps. This not only synthesizes more data, but also makes the trained ConvNets view-tolerant. Second, WHDMMs at several temporal scales are constructed to encode the spatiotemporal motion patterns of actions into 2-D spatial structures. The 2-D spatial structures are further enhanced for recognition by converting the WHDMMs into pseudocolor images. Finally, the three ConvNets are initialized with the models obtained from ImageNet and fine-tuned independently on the color-coded WHDMMs constructed in three orthogonal planes. The proposed algorithm was evaluated on the MSRAction3D, MSRAction3DExt, UTKinect-Action, and MSRDailyActivity3D datasets using cross-subject protocols. In addition, the method was evaluated on the large dataset constructed from the above datasets. The proposed method achieved 2–9 better results on most of the individual datasets. Furthermore, the proposed method maintained its performance on the large dataset, whereas the performance of existing methods decreased with the increased number of actions.