Object Positioning Algorithm Based on Multidimensional Scaling and Optimization for Synthetic Gesture Data Generation

Performance Optimization of Object Tracking Algorithms in OpenCV on GPUs

Machine-learning-based computer vision is increasingly versatile and being leveraged by a wide range of smart devices. Due to the limited performance/energy budget of computing units in smart devices, the careful implementation of computer vision algorithms is critical. In this paper, we analyze the performance bottleneck of two well-known computer vision algorithms for object tracking: object detection and optical flow in the Open-source Computer Vision library (OpenCV). Based on our in-depth analysis of their implementation, we found the current implementation fails to utilize Open Computing Language (OpenCL) accelerators (e.g., GPUs). Based on the analysis, we propose several optimization strategies and apply them to the OpenCL implementation of object tracking algorithms. Our evaluation results demonstrate the performance of the object detection is improved by up to 86% and the performance of the optical flow by up to 10%. We believe our optimization strategies can be applied to other computer vision algorithms implemented in OpenCL.

Air-Writing Character Recognition with Ultrasonic Transceivers

The interfaces between users and systems are evolving into a more natural communication, including user gestures as part of the interaction, where air-writing is an emerging application for this purpose. The aim of this work is to propose a new air-writing system based on only one array of ultrasonic transceivers. This track will be obtained based on the pairwise distance of the hand marker with each transceiver. After acquiring the track, different deep learning algorithms, such as long short-term memory (LSTM), convolutional neural networks (CNN), convolutional autoencoder (ConvAutoencoder), and convolutional LSTM have been evaluated for character recognition. It has been shown how these algorithms provide high accuracy, where the best result is extracted from the ConvLSTM, with 99.51% accuracy and 71.01 milliseconds of latency. Real data were used in this work to evaluate the proposed system in a real scenario to demonstrate its high performance regarding data acquisition and classification.