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DASOT: A Unified Framework Integrating Data Association and Single Object Tracking for Online Multi-Object Tracking
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
In this paper, we propose an online multi-object tracking (MOT) approach that integrates data association and single object tracking (SOT) with a unified convolutional network (ConvNet), named DASOTNet. The intuition behind integrating data association and SOT is that they can complement each other. Following Siamese network architecture, DASOTNet consists of the shared feature ConvNet, the data association branch and the SOT branch. Data association is treated as a special re-identification
doi:10.1609/aaai.v34i07.6694
fatcat:vsxn4dqbsnhsjbwz6cvfmgusfy