Multiple object tracking based on quadratic graph matching - Jiayan Gao; Qi Zou; Hongwei Zhao (IET Computer Vision, 2023)
Abstract Recently, with the development of deep‐learning, the performance of multiple object tracking (MOT) algorithm based on deep neural networks has been greatly improved. However, it is still a difficult problem to successfully solve the tracking misalignment caused by occlusion and complex tracking scenes. Most of the work focusses on designing a sophisticated network, only little work focusses on data association. Actually, data association is very helpful in MOT. In this study, data association in tracking is taken as the main research task, and the authors introduce quadratic graph matching into MOT. Considering the objects in each frame as a graph, we can model the data association between two frames as a quadratic graph matching problem. And then it is transformed into a convex quadratic optimisation problem. Introducing high‐order structure features into the matching function effectively solves some tracking problems. The data association is designed into the tracking model as an overall solution. Experiments show that the proposed data association algorithm performs favourably against several state‐of‐the‐art data association methods and can be used in any tracking‐by‐detection method. Our code is publicly available from https://github.com/gjy0514/G_Model.