Real-time pedestrian recognition on low computational resources
Guifan Weng
Pedestrian recognition has successfully been applied to security, autonomous
cars, Aerial photographs. For most applications, pedestrian recognition on
small mobile devices is important. However, the limitations of the computing
hardware make this a challenging task. In this work, we investigate real-time
pedestrian recognition on small physical-size computers with low computational
resources for faster speed. This paper presents three methods that work on the
small physical size CPUs system. First, we improved the Local Binary Pattern
(LBP) features and Adaboost classifier. Second, we optimized the Histogram of
Oriented Gradients (HOG) and Support Vector Machine. Third, We implemented fast
Convolutional Neural Networks (CNNs). The results demonstrate that the three
methods achieved real-time pedestrian recognition at an accuracy of more than
95% and a speed of more than 5 fps on a small physical size computational
platform with a 1.8 GHz Intel i5 CPU. Our methods can be easily applied to
small mobile devices with high compatibility and generality.