Towards Open World Person Re Identification by One Shot Group Based Verification
Solving the problem of matching people across non overlapping multi camera views, known as person re identification (re id) has received increasing interests in computer vision. In a real world application scenario, a watch list (gallery set) of a handful of known target people are provided with very few (in many cases only a single) image(s) (shots) per target. Existing re id methods are largely unsuitable to address this open world re id challenge because they are designed for (1) a closed world scenario where the gallery and probe sets are assumed to contain exactly the same people, (2) person wise identification whereby the model attempts to verify exhaustively against each individual in the gallery set, and (3) learning a matching model using multi shots. In this paper, a novel transfer local relative distance comparison (t LRDC) model is formulated to address the open world person re identification problem by one shot group based verification. The model is designed to mine and transfer useful information from a labelled open world non target dataset. Extensive experiments demonstrate that the proposed approach outperforms both non transfer learning and existing transfer learning based re id methods.
IEEE Transactions on Pattern Analysis and Machine Intelligence