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We propose a method for real-time people tracking using multiple cameras. The particle filter framework is known to be effective for tracking people, but most of existing methods adopt only simple perceptual cues such as color histogram or contour similarity for hypothesis evaluation. To improve the robustness and accuracy of tracking more sophisticated hypothesis evaluation is indispensable. We therefore present a novel technique for human head tracking using cascaded classifiers based on AdaBoost and Haar-like features for hypothesis evaluation. In addition, we use multiple classifiers, each of which is trained respectively to detect one direction of a human head. During real-time tracking the most suitable classifier is adaptively selected by considering each hypothesis and known camera position. Our experimental results demonstrate the effectiveness and robustness of our method.

We propose a novel method to synthesize a noise- and blur-free color image sequence using near-infrared (NIR) images captured in extremely low light conditions. In extremely low light scenes, heavy noise and motion blur are simultaneously produced in the captured images. Our goal is to enhance the color image sequence of an extremely low light scene. In this paper, we augment the imaging system as well as enhancing the image synthesis scheme. We propose a novel imaging system that can simultaneously capture the red, green, blue (RGB) and the NIR images with different exposure times. An RGB image is taken with a long exposure time to acquire sufficient color information and mitigates the effects of heavy noise. By contrast, the NIR images are captured with a short exposure time to measure the structure of the scenes. Our imaging system using different exposure times allows us to ensure sufficient information to reconstruct a clear color image sequence. Using the captured image pairs, we reconstruct a latent color image sequence using an adaptive smoothness condition based on gradient and color correlations. Our experiments using both synthetic images and real image sequences show that our method outperforms other state-of-the-art methods.

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