Robust Head-Shoulder Detection by PCA-Based Multilevel HOG-LBP Detector for People Counting

Robustly counting the number of people for surveillance systems has widespread applications. In this paper, we propose a robust and rapid head-shoulder detector for people counting. By combining the multilevel HOG (Histograms of Oriented Gradients) with the multilevel LBP (Local Binary Pattern) as the feature set, we can detect the head-shoulders of people robustly, even though there are partial occlusions occurred. To further improve the detection performance, Principal Components Analysis (PCA) is used to reduce the dimension of the multilevel HOG-LBP feature set. Our experiments show that the PCA based multilevel HOG-LBP descriptors are more discriminative, more robust than the state-of-the-art algorithms. For the application of the real-time people-flow estimation, we also incorporate our detector into the particle filter tracking and achieve convincing accuracy.