This paper proposes a simulation approach to detect different human poses in real time with streaming data. Pose detection in real time is a critical area for many of the applications in different domains where the available literature deals with training-based models on huge amount of data and the methods use 3D cameras for accurate predictions. It also requires huge computational efforts and GPU machines to obtain different human poses in real time. The available methods in the literature are with high frame rate requirement and use previous frames as well for predicting the poses of human. If the frame rate is less, then the methods fail to predict the poses accurately and efficiently. The proposed simulation mechanism describes the simulation of different poses of human and generates feature descriptors for each of the pose and trains the model using simple classifier. The trained model predicts the real-time human pose detection on video streaming data. The different poses are predicted with less frame rate using simple 2D cameras and with accurate predictions by reducing the processing time and with less computational efforts. The proposed solution will be used to predict the candidate poses or gestures in the virtual interview application.
Establishing a remote assistance system which will guide the user in repair or maintenance works of any manufacturer machine is quite challenging. The present paper proposes a solution where the user will train a deep learning models to detect different devices and devices parts. The paper discusses different solutions used in training the models and compares these models in detecting the objects of devices with respect to accuracy and time. The tracking and real-time detection of the objects help the user to perform any remote task with the help of an expert. The solution is ported in all types of mobile devices where users can see the devices and devices parts with augmented information on top of that. This solution will help the user to take remote tasks with the help of expert where expert cannot reach the field for repair or maintenance of devices. The entire solution is deployed for dialysis machine use case for performing multiple repair or maintenance activities of the dialysis machine. The accuracy of these models and model performance in real-time also play a key role as part of our remote assistance tasks.
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