Comparing SVM and SSD for classification of vehicles and pedestrians for edge computing

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 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.