Recently, skeleton-based gesture and action recognition have emerged thanks to the progress in human pose estimation. Gesture representation using skeletal data is robust since skeletal data are invariant to the individual's appearance. Among different approaches proposed for skeleton-based action/gesture recognition, Graph Convolutional Network (GCN) and its variations have obtained great attention thanks to its ability to capture the graph essence of the skeletal data. In this paper, we aim to design an efficient scheme using relative joints of skeleton sequences adapted in a GCN framework. Both spatial features (i.e., joint positions) and temporal ones (i.e., the velocity of joints) are combined to form the input of Attention-enhanced Adaptive GCN (AAGCN). The proposed framework deals with limitations of the original AAGCN when it works on challenging datasets with incomplete and noisy skeletal data. Extensive experiments are carried out on three datasets CMDFALL, MICA-Action3D, NTU-RGBD. Experimental results show that the proposed method achieves superior performance compared with existing methods. Moreover, to illustrate the application of the proposed method in real-time traffic control gesture recognition for autonomous vehicles, we have evaluated the proposed method on the TCG dataset. The obtained results show that the proposed method offers real-time computation capability and good recognition results. These results suggest a promising solution to deploy a real-time and robust recognition technique for gesture-based traffic control in autonomous vehicles.

In recent years, human action recognition (HAR) has been an attractive research topic in computer vision since HAR has been widely applied in various fields, such as gaming, healthcare, surveillance, and human-machine interaction. In this paper, we present a feasible solution to develop a real-time HAR application that helps one monitor physical exercises. As an end-to-end solution, the proposed framework consists of techniques that recognize the physical exercises, spot each in real-time and assess the quality of the practicing users. Firstly, we define nine common physical exercises, such as arm circles, squats, jumping jacks, etc. We then construct a dataset named COMVIS-FITNESS consisting of the physical exercises dataset of nine subjects. We argue the advantages of two different deep neural networks for HAR to recognize the exercises. One is a compact HAR neuronal network named DD-Net, and another is a high-performance graph convolutional neural network called FF-AAGCN. For spotting the exercises from an image sequence, a sliding window method is combined with these two networks to make a real-time classification. A technique to evaluate the workout of the practicing user is proposed. Experimental results on COMVIS-FITNESS show that deep neural networks can recognize all exercises in the datasets with high accuracy. The accuracies and F1-scores of DD-Net are 99.24% and 99.23%, while those obtained by FF-AAGCN are 98.48% and 98.32%, respectively. The completed pipeline of the proposed method is integrated in an application. The application works in real-time on edge device as Jetson Xavier AGX. The proposed techniques and dataset are made publicly available and can be downloaded from https://bit.ly/3F9J1qb.

Bee counting is an essential task for monitoring the health of bee colonies. However, it is challenging, as bees are often small and difficult to see. One approach to bee counting is detecting individuals and using that information to count. However, this approach can be unreliable if the data are noisy, meaning some labels are incorrect. This paper presents a novel approach to bee counting with noise-labeled data. The proposed method uses a density-based counter that is robust with noisy data. Experimental results show that the proposed method can achieve significantly higher accuracy than the detection-based approach for bee counting with noise-labeled data. The proposed method can improve the accuracy of bee counting in various scenes, such as different weather conditions and the density of bees. The approach could also be used to develop new tools for monitoring bee populations. Source codes of the proposed method and the dataset are publicly available on GitHub.