A bottom-up, view-point invariant human detector

We propose a bottom-up human detector that can deal with arbitrary poses and viewpoints. Heads, limbs and torsos are individually detected, and an efficient assembly strategy is used to perform the human detection and the part segmentation. Firstly, a topological model is used to represent the structure of the human body, and the topologically equivalent configurations are ranked with additional priors. Promising results prove the approach efficiency for detecting people in low-resolution and compressed images.