Hand tracking by binary quadratic programming and its application to retail activity recognition

Substantial ambiguities arise in hand tracking due to issues such as small hand size, deformable hand shapes and similar hand appearances. These issues have greatly limited the capability of current multi-target tracking techniques in hand tracking. As an example, state-of-the-art approaches for people tracking handle indentity switching by exploiting the appearance cues using advanced object detectors. For hand tracking, such approaches will fail due to similar, or even identical hand appearances. The main contribution of our work is a global optimization framework based on binary quadratic programming (BQP) that seamlessly integrates appearance, motion and complex interactions between hands. Our approach effectively handles key challenges such as occlusion, detection failure, identity switching, and robustly tracks both hands in two challenging real-life scenarios: retail surveillance and sign languages. In addition, we demonstrate that an automatic method based on hand trajectory analysis outperforms state-of-the-art on checkout-related activity recognition in grocery stores.