Video‐based person re‐identification based on regularised hull distance learning

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Existing person re‐identification (re‐id) models mainly focus on still‐image‐based module, namely matching person images across non‐overlapping camera views. Since video sequence contains much more information than still images and can be easily achieved by tracking algorithms in practical applications, the video re‐id has attracted increasing attention in recent years. Distance learning is crucial for a re‐id system. However, the computed distances in traditional video‐based methods are easily distracted by the randomness of data distribution, especially with small sample size for training. To preferably distinguish different people, a novel regularised hull distance learning video‐based person re‐id method is proposed. It is advantageous in two aspects: robustness is guaranteed due to expanded video samples by regularised affine hull with limited ones, discriminability is ensured due to penalised hard negative samples more severely. Hence, the discriminability and robustness of the learnt metric are strengthened. Comparisons with the state‐of‐the‐art video‐based methods as well as related methods on PRID 2011, iLIDS‐VID and MARS datasets demonstrate the superiority of the authors’ method.