Human Detection in Video Surveillance using Texture Features

This research presents a method for human detection at night in video surveillance camera. The process of detecting human at night is very challenging due to certain factors such as radiance, silhouette and low external light. A comparative study between three texture features that are Discrete Wavelet Transform (DWT), Histogram of Oriented Gradient (HOG) and Speeded Up Robust Feature (SURF) using Support Vector Machine (SVM), Naive Bayes and Adaboost classifiers are investigated using primary data extracted from a video surveillance camera at the faculty. The results show that HOG feature with Naive Bayes detect human in video surveillance better compared to DWT and SURF with SVM and AdaBoost classifiers.