Visual tracking via Graph Regularized Kernel Correlation Filer and Multi-Memory Voting

Abstract

Correlation filter based tracking approach has been an important branch of visual tracking. However, most correlation filter based trackers fail to work under occlusion due to their frame-by-frame model update strategy, and the tracking performance can be further enhanced by optimizing the energy equation. The target appearance during tracking is nearly moving on a manifold. So, the classification scores should be similar on the target manifold. K Nearest Neighbor graphs are constructed and the classification scores on the neighborhood are regularized to have similar values. Through the local score propagation on the graph, the learned Graph Regularized Kernel Correlation Filer can represent different appearances of the object. Furthermore, in the proposed Multi-Memory Voting scheme, occlusion problem is addressed by voting from multiple target snapshots in the memory pool. An extensive evaluation on two recent benchmarks shows that the proposed tracker achieves competitive performance compared to nine other state-of-the-art trackers.