SVM and SSD for Classification of Mobility Actors on the Edge

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. We compare two SVM bases techniques, IPHOG and a MBF using Scale Invariant Features. The obtained results show that with a video resolution of 1280 × 720 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:

• Raspberry PI
• Convolutional neural network
• Performance evaluation
• Support Vector Machine
• Computer Vision
• Single Shot Detection

Fuente:

scopus