1 research outputs found
Videoābased person reāidentification based on regularised hull distance learning
Existing person reāidentification (reāid) models mainly focus on stillāimageābased module, namely matching person images across nonāoverlapping 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 reāid has attracted increasing attention in recent years. Distance learning is crucial for a reāid system. However, the computed distances in traditional videoābased 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 videoābased person reāid 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 stateāofātheāart videoābased methods as well as related methods on PRID 2011, iLIDSāVID and MARS datasets demonstrate the superiority of the authorsā method