Proposing Gesture Recognition Algorithm Using HOG and SVM for Smart-Home Applications

In this study, we propose the gesture recognition algorithm using support vector machines (SVM) and histogram of oriented gradient (HOG). Besides, we also use the CNN model to classify gestures. We approach and select techniques of applying problem controlling for the robotic system. The goal of the algorithm is to detect gestures with real-time processing speed, minimize interference, and reduce the ability to capture unintentional gestures. Static gesture controls are used in this study including on, off, increasing, and decreasing. Besides, it uses motion gestures including turning on the status switch and increasing and decreasing the volume. Results show that the algorithm is up to 99% accuracy with a 70-millisecond execution time per frame that is suitable for industrial applications.

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Hand Gesture Recognition Algorithm Using SVM and HOG Model for Control of Robotic System

Huu

Ngoc

2021

Journal of Robotics

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RNN- and CNN-based weed detection for crop improvement: An overview

Jabir

Rabhi

Falih

2021

Foods and Raw Materials

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Introduction. Deep learning is a modern technique for image processing and data analysis with promising results and great potential. Successfully applied in various fields, it has recently entered the field of agriculture to address such agricultural problems as disease identification, fruit/plant classification, fruit counting, pest identification, and weed detection. The latter was the subject of our work. Weeds are harmful plants that grow in crops, competing for things like sunlight and water and causing crop yield losses. Traditional data processing techniques have several limitations and consume a lot of time. Therefore, we aimed to take inventory of deep learning networks used in agriculture and conduct experiments to reveal the most efficient ones for weed control. Study objects and methods. We used new advanced algorithms based on deep learning to process data in real time with high precision and efficiency. These algorithms were trained on a dataset containing real images of weeds taken from Moroccan fields. Results and discussion. The analysis of deep learning methods and algorithms trained to detect weeds showed that the Convolutional Neural Network is the most widely used in agriculture and the most efficient in weed detection compared to others, such as the Recurrent Neural Network. Conclusion. Since the Convolutional Neural Network demonstrated excellent accuracy in weed detection, we adopted it in building a smart system for detecting weeds and spraying them in place.

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Proposing Gesture Recognition Algorithm Using HOG and SVM for Smart-Home Applications

Cited by 2 publications

References 9 publications

Hand Gesture Recognition Algorithm Using SVM and HOG Model for Control of Robotic System

Hand Gesture Recognition Algorithm Using SVM and HOG Model for Control of Robotic System

RNN- and CNN-based weed detection for crop improvement: An overview

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