Haarlike Feature Revisited: Fast Human Detection Based on Multiple Channel Maps
Xin Zuo, Jifeng Shen, Hualong Yu, Yuanyuan Dan · Lecture notes in computer science · 2015
Haarlike feature has achieved great success in detecting frontal human faces, but fewer attentions have been paid to the other objects such as pedestrian. The reason of the low detection rate for Haarlike feature is attributed to the usage in a naive way. In this paper, we have revisited Haarlike feature for object detection especially focus on pedestrians, but use it in a different way which is applied based on multiple channel maps instead of raw pixels and obtains a significant improvement. Furthermore, we have proposed an improved Haarlike feature that embeds statistical information from the training data which is based on the linear discriminative analysis criterion. The proposed feature works with the classical Gentle Boosting algorithm which is effective in training, and also running at real-time speed. Experiments based on INIRA dataset demonstrate that our proposed method is easy to implement and achieves the performance comparable to the state-of-the-arts.