  Real-time object detection and tracking have shown to be the basis of
intelligent production for industrial 4.0 applications. It is a challenging
task because of various distorted data in complex industrial setting. The
correlation filter (CF) has been used to trade off the low-cost computation and
high performance. However, traditional CF training strategy can not get
satisfied performance for the various industrial data; because the simple
sampling(bagging) during training process will not find the exact solutions in
a data space with a large diversity. In this paper, we propose
Dijkstra-distance based correlation filters (DBCF), which establishes a new
learning framework that embeds distribution-related constraints into the
multi-channel correlation filters (MCCF). DBCF is able to handle the huge
variations existing in the industrial data by improving those constraints based
on the shortest path among all solutions. To evaluate DBCF, we build a new
dataset as the benchmark for industrial 4.0 application. Extensive experiments
demonstrate that DBCF produces high performance and exceeds the
state-of-the-art methods. The dataset and source code can be found at
https://github.com/bczhangbczhang
