Fast Target Redetection for CAMSHIFT using Back-projection and Histogram Matching

Most visual tracking algorithms lose track of the target object (start tracking a different object or part of the background) or report an error when the object being tracked leaves the scene or becomes occluded in a cluttered environment. We propose a fast algorithm for mobile robots tracking humans or other objects in real-life scenarios to avoid these problems. The proposed method uses an adaptive histogram threshold matching algorithm to suspend the CAMSHIFT tracker when the target is insufficiently clear. While tracking is suspended, any method would need to continually scan the entire image in an attempt to redetect and reinitialize tracking of the specified object. However, searching the entire image for an arbitrary target object requires an extremely efficient algorithm to be feasible in real time. Our method, rather than a detailed search over the entire image, makes efficient use of the backprojection of the target object's appearance model to hypothesize and test just a few candidate locations for the target in each image. Once the target object is redetected and sufficiently clear in a new image, the method reinitializes tracking. In a series of experiments with four real-world videos, we find that the method is successful at suspending and reinitializing CAMSHIFT tracking when the target leaves and reenters the scene, with successful reinitialization and very low false positive rates.