A Reliable People Counting System via Multiple Cameras
ACM Transactions on Intelligent Systems and Technology (TIST)
Pedestrian detection by PCA-based mixed HOG-LBP features
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Automatic scene calibration for detecting and tracking people using a single camera
Engineering Applications of Artificial Intelligence
People counting by learning their appearance in a multi-view camera environment
Pattern Recognition Letters
A framework for improved video text detection and recognition
Multimedia Tools and Applications
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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