5 March 2014 Object instance recognition using motion cues and instance specific appearance models
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In this paper we present an object instance retrieval approach. The baseline approach consists of a pool of image features which are computed on the bounding boxes of a query object track and compared to a database of tracks in order to find additional appearances of the same object instance. We improve over this simple baseline approach in multiple ways: 1) we include motion cues to achieve improved robustness to viewpoint and rotation changes, 2) we include operator feedback to iteratively re-rank the resulting retrieval lists and 3) we use operator feedback and location constraints to train classifiers and learn an instance specific appearance model. We use these classifiers to further improve the retrieval results. The approach is evaluated on two popular public datasets for two different applications. We evaluate person re-identification on the CAVIAR shopping mall surveillance dataset and vehicle instance recognition on the VIVID aerial dataset and achieve significant improvements over our baseline results.
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Arne Schumann, Arne Schumann, "Object instance recognition using motion cues and instance specific appearance models", Proc. SPIE 9026, Video Surveillance and Transportation Imaging Applications 2014, 90260S (5 March 2014); doi: 10.1117/12.2038541; https://doi.org/10.1117/12.2038541


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