Object tracking using learned feature manifolds

Local feature based object tracking approaches have been promising in solving the tracking problems such as occlusions and illumination variations. However, existing approaches typically model feature variations using prototypes, and this discrete representation cannot capture the gradual changing property of local appearance. In this paper, we propose to model each local feature as a feature manifold to characterize the smooth changing behavior of the feature descriptor. The manifold is constructed from a series of transformed images simulating possible variations of the feature being tracked. We propose to build a collection of linear subspaces which approximate the original manifold as a low dimensional representation. This representation is used for object tracking. Object location is located by a feature-to-manifold matching process. Our tracking method can update the manifold status, add new feature manifolds and remove expiring ones adaptively according to object appearance. We show both qualitatively and quantitatively this representation significantly improves the tracking performance under occlusions and appearance variations using standard tracking dataset. Object tracking is a central problem in computer vision with many applications, such as activity analysis, automated surveillance , traffic monitoring, and human-computer interaction. It is essentially the problem of finding the most likely estimate of the object state given a sequence of observations. Object tracking is challenging because of: Complex object appearance. The object may have complicated appearance which is hard to model. Furthermore, it may undergo significant changes due to the pose and scale variations as well as non-rigid object motions. Occlusions. The object may be occluded by the background or other moving objects, making it difficult to be localized. Complex object motion. This is caused by either the moving pattern of the object or by camera motion accompanied by object motion. There are two key components in an object tracking algorithm: object representation and dynamics. Object representation tries to model the object as accurately as possible so that the tracking algorithm can accurately describe the complex object appearance. Object dynamics model how the object appearance evolves over time to be able to handle appearance variations. The two problems are usually coupled together: the object representation should be designed to be easily updated to model appearance variations, while the object dynamics should be able to take advantage of the characteristics of object representation for model update. Traditional methods for representing the object, such as global histogram based approach in meanshift tracking [1] and PCA sub-space based …