Global optimal data association for multiple people tracking

Abstract

Ahstract-Multiple people tracking is an important compo nent for different tasks such as video surveillance and human robot interaction. In this paper, a global optimization approach is proposed for long-term tracking of an a priori unknown num ber of targets, particularly aim to improve the robustness in case o�' comrle � interaction and mutual occlusion. With a state-space dIscretIzatIOn scheme, the multiple object tracking problem is formulated with a grid-based network flow model, resulting in a convex problem that can be casted into an Integer Linear Programming (lLP), then solved through relaxation. In order to allow recovery from misdetections, common heuristics such as non-maxima suppression is eschewed within observations.In addition, we show that how behavior cue can be integrated into the association affinity model, providing discriminative hints for resolving ambiguities between crossing trajectories.The validity of the proposed method is demonstrated through ex�eriments on multiple challenging video sequences, using a calIbrated multi-camera setup.

Workspace monitoring and planning for safe mobile manipulation

“…Moreover, multiple sensors are usually required to achieve the desired spatial coverage. These can be homogeneous sensors with complementary fields of view, as usually employed in the literature [4], or heterogeneous sensors with considerably different range, resolution, or even sensing principle. In the latter case, particular attention must be paid to the association step in order to achieve the desired robustness for objects moving from one sensor's field of view to a different sensor's field of view.…”