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A reliable system for recognition and tracking of a moving target in nonuniformly illuminated scenes is presented. The system employs a filter bank of space-variant correlation filters adapted to local statistical parameters of the observed scene in each frame. When a scene frame is captured, a fragment of interest is constructed in the frame around predicted location of the target based on a kinematic model. The fragment is firstly pointwise processed to correct the illumination. Afterwards, the state of the target is estimated from the restored fragment by employing a bank of space-variant correlation filters. The performance of the proposed system in terms of object recognition and tracking is tested with nonuniformly illuminated and noisy scenes. The results are compared with those of common techniques based on correlation filtering.

Object Tracking in Nonuniform Illumination Using Space-Variant Correlation Filters