Comparing SVM and SSD for classification of vehicles and pedestrians for edge computing
Abstract:
In the context of video processing, transmission to a remote server is not always possible nor suitable. Video processing on the edge could offer a solution. However, lower processing capacities constraint the number of techniques available for devices, in this work we report the performance of two techniques for classification from video on a minicomputer. The implementation of a real-time vehicle counting and classification system is evaluated through Support Vector Machine (SVM) and the Single Shot Detector Framework (SSD) in a minicomputer. The obtained results show that with a video resolution of 1280x720 pixels and using SVM, precision and recognition rates of 86 % and 94 % are obtained respectively, while with SSD 93 % and 67 % rates are reached with times of processing higher than SVM.
Año de publicación:
2019
Keywords:
- Support Vector Machine
- Raspberry PI
- Performance evaluation
- Convolutional neural network
- Computer Vision
- Single Shot Detection
Fuente:
scopusTipo de documento:
Conference Object
Estado:
Acceso restringido
Áreas de conocimiento:
- Aprendizaje automático
- Ciencias de la computación
- Simulación por computadora
Áreas temáticas:
- Métodos informáticos especiales
- Funcionamiento de bibliotecas y archivos
- Física aplicada
