Crowd analysis in non-static cameras using feature tracking and multi-person density

We propose a new methodology for crowd analysis by introducing the concept of Multi-Person Density. Using a state-of-the-art feature tracking algorithm, representative low-level features and their long-term motion information are extracted and combined into a human detection model. In contrast to previously proposed techniques, the proposed method takes small camera motion into account and is not affected by camera shaking. This increases the robustness of separating crowd features from background and thus opens a whole new field for application of these techniques in non-static CCTV cameras. We show the effectiveness of our approach on various test videos and compare it to state-of-the-art people counting methods.