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In real-time tracking, a crucial challenge is to efficiently build association among the objects. However, realtime interferences (e.g. occlusion) manifest errors in data association. In this paper, the uncertainties in data association are handled when discrete information is incomplete during occlusion through qualitative reasoning modules. The formulation of the qualitative modules are based on exploiting human-tracking abilities (i.e. common sense) which are integrated with data association technique. Each detected object is described as a node in space with a unique identity and status tag whereas association weights are computed using CWHI and Bhattacharyya coefficient. These weights are input to qualitative modules which interpret the appropriate status of the objects satisfying the fundamental constraints of object’s continuity during tracking. The results are linked with Kalman Filter to estimate the trajectories of objects. The proposed approach has shown promising results illustrating its contribution when tested on a set of videos representing various challenges.

RESOLVING DATA-ASSOCIATION UNCERTAINTY - In Mutli-object Tracking through Qualitative Modules