Exploring 3D Human Action Recognition Using STACOG on Multi-View Depth Motion Maps Sequences

Deep Learning-Based Human Action Recognition with Key-Frames Sampling Using Ranking Methods

Nowadays, the demand for human–machine or object interaction is growing tremendously owing to its diverse applications. The massive advancement in modern technology has greatly influenced researchers to adopt deep learning models in the fields of computer vision and image-processing, particularly human action recognition. Many methods have been developed to recognize human activity, which is limited to effectiveness, efficiency, and use of data modalities. Very few methods have used depth sequences in which they have introduced different encoding techniques to represent an action sequence into the spatial format called dynamic image. Then, they have used a 2D convolutional neural network (CNN) or traditional machine learning algorithms for action recognition. These methods are completely dependent on the effectiveness of the spatial representation. In this article, we propose a novel ranking-based approach to select key frames and adopt a 3D-CNN model for action classification. We directly use the raw sequence instead of generating the dynamic image. We investigate the recognition results with various levels of sampling to show the competency and robustness of the proposed system. We also examine the universality of the proposed method on three benchmark human action datasets: DHA (depth-included human action), MSR-Action3D (Microsoft Action 3D), and UTD-MHAD (University of Texas at Dallas Multimodal Human Action Dataset). The proposed method secures better performance than state-of-the-art techniques using depth sequences.

Edge Detection-Based Feature Extraction for the Systems of Activity Recognition

Human activity recognition (HAR) is a fascinating and significant challenging task. Generally, the accuracy of HAR systems relies on the best features from the input frames. Mostly, the activity frames have the hostile noisy conditions that cannot be handled by most of the existing edge operators. In this paper, we have designed an adoptive feature extraction method based on edge detection for HAR systems. The proposed method calculates the direction of the edges under the presence of nonmaximum conquest. The benefits are in ease that depends upon the modest procedures, and the extension possibility is to determine other types of features. Normally, it is practical to extract extra low-level information in the form of features when determining the shapes and to get the appropriate information, the additional cultured shape detection procedure is utilized or discarded. Basically, this method enlarges the percentage of the product of the signal-to-noise ratio (SNR) and the highest isolation along with localization. During the processing of the frames, again some edges are demonstrated as a footstep function; the proposed approach might give better performance than other operators. The appropriate information is extracted to form feature vector, which further be fed to the classifier for activity recognition. We assess the performance of the proposed edge-based feature extraction method under the depth dataset having thirteen various kinds of actions in a comprehensive experimental setup.