Abstract
In robot vision, it is important to recognize moving objects in dynamic image, and omni-directional camera is effective for this purpose. Omni-directional camera has an advantage that more visual information can be obtained at the same time than human. In this work we propose an approach of tracking multiple moving objects (human etc.) in dynamic image of omni-directional camera. The dynamics of position and velocity of target object and measurement process of the object by omni-directional camera are represented by a state space model based on finite random set. In general, a scene may have occlusion and appearance of objects, and the multiple moving objects may be observed with missing and false detections. Finite random set is suitable for representing these situations. State estimation is performed by Sequential Monte Carlo (SMC) implementation of Probability Hypothesis Density (PHD) filter. The multiple moving objects are simultaneously tracked by the filter. Numerical simulation and real image experiment results show tracking performance of proposed method.
