Comparing Deep Neural Networks and Traditional Vision Algorithms in Mobile Robotics

We consider the problem of object detection on a mobile robot by comparing and contrasting two types of algorithms for computer vision. The first approach is coined ”traditional computer vision” and refers to using commonly known feature descriptors (SIFT, SURF, BRIEF, etc.) for object detection. The second approach uses Deep Neural Networks for object detection. We show that deep neural networks perform better than traditional algorithms, but discuss major trade offs surrounding performance and training time.