Fast Bayesian People Detection

Template-based methods have been shown to be effective at solving the problem of tracking specific objects, but their large number of free parameters can make them slow to apply and hard to optimise globally. In this work, we propose a template-based method for tracking people with fixed cameras, which automatically detects the number of people in a frame, is robust to occlusions, and can run at near-real-time frame rates. We demonstrate the effectiveness of the method by comparing it to a state-of-the-art background segmentation algorithm and show its important performance advantage

People counting with stereo cameras:two template-based solutions

People counting is a challenging task with many applications. We propose a method with a fixed stereo camera that is based on projecting a template onto the depth image. The method was tested on a challenging outdoor dataset with good results and runs in real time