Hand Gesture Recognition System for Parking Applications based on Machine Vision
In this paper, we aim to combine the best features of image recognition along with embedded control system. The system completes the image preprocessing and recognizes the gesture information, and then feeds the recognition results back to the Raspberry Pi through the Bluetooth device. Raspberry Pi sends the corresponding gesture number to STC89C52 according to the recognition result, and STC89C52 converts the gesture number into a motion instruction and sends it to the machine to control the movement of car. For the choice of gestures, we use five different gestures to control the car's forward, backward, turn left.turn right, and stop. The geometric moment feature and histogram feature of the preprocessed gesture image are extracted, and the first two components of the seven feature components and the compactness of the preprocessed gesture image are selected as feature values. Anaconda distribution of Python is used to build a BP neural network, and takes feature value as input. After the BP neural network outputs the recognition results, the embedded system converts the recognition results into control instructions to control the the car movement.
2020 IEEE 3rd International Conference on Electronic Information and Communication Technology (ICEICT)