Real-Time Tracking of Fast-Moving Object in Occlusion Scene

Tracking the fast-moving object in occlusion situations is an important research topic in computer vision. Despite numerous notable contributions have been made in this field, few of them simultaneously incorporate both object's extrinsic features and intrinsic motion patterns into their methodologies, thereby restricting the potential for tracking accuracy improvement. In this paper, on the basis of efficient convolution operators (ECO) model, a speed-accuracy-balanced model is put forward. This model uses the simple correlation filter to track the object in real-time, and adopts the sophisticated deep-learning neural network to extract high-level features to train a more complex filter correcting the tracking mistakes, when the tracking state is judged to be poor. Furthermore, in the context of scenarios involving regular fast-moving, a motion model based on Kalman filter is designed which greatly promotes the tracking stability, because this motion model could predict the object's future location from its previous movement pattern. Additionally, instead of periodically updating our tracking model and training samples, a constrained condition for updating is proposed, which effectively mitigates contamination to the tracker from the background and undesirable samples avoiding model degradation when occlusion happens. From comprehensive experiments, our tracking model obtains better performance than ECO on object tracking benchmark 2015 (OTB100), and improves the area under curve (AUC) by about 8% and 32% compared with ECO, in the scenarios of fast-moving and occlusion on our own collected dataset.