SLTP: A Fast Descriptor for People Detection in Depth Images

Abstract

This paper presents a new feature descriptor for real-time people detection in depth images. The shape cue in depth images can reduce negative impacts of variations of clothing, lighting conditions and the complexity of backgrounds. The proposed Simplified Local Ternary Patterns (SLTP) can take advantage of depth images to describe human body shape with low computational cost. To evaluate the SLTP feature, we establish a dataset with 7260 positive samples. A series of experiments are carried out on this dataset, and the results show that the SLTP feature can achieve a high detection rate with a low false positive rate. Besides, SLTP is easy to implement, and performs fast (over 80 frames per second) on a standard desktop computer.