Attribute-restricted latent topic model for person re-identification
Pattern Recognition
Person re-identification in crowd
Pattern Recognition Letters
Intelligent multi-camera video surveillance: A review
Pattern Recognition Letters
Set based discriminative ranking for recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Relaxed pairwise learned metric for person re-identification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Person re-identification: what features are important?
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Local descriptors encoded by fisher vectors for person re-identification
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Re-identification with RGB-D sensors
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Combinational subsequence matching for human identification from general actions
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
A framework for inter-camera association of multi-target trajectories by invariant target models
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
People reidentification in surveillance and forensics: A survey
ACM Computing Surveys (CSUR)
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Matching people across non-overlapping camera views, known as person re-identification, is challenging due to the lack of spatial and temporal constraints and large visual appearance changes caused by variations in view angle, lighting, background clutter and occlusion. To address these challenges, most previous approaches aim to extract visual features that are both distinctive and stable under appearance changes. However, most visual features and their combinations under realistic conditions are neither stable nor distinctive thus should not be used indiscriminately. In this paper, we propose to formulate person re-identification as a distance learning problem, which aims to learn the optimal distance that can maximises matching accuracy regardless the choice of representation. To that end, we introduce a novel Probabilistic Relative Distance Comparison (PRDC) model, which differs from most existing distance learning methods in that, rather than minimising intra-class variation whilst maximising intra-class variation, it aims to maximise the probability of a pair of true match having a smaller distance than that of a wrong match pair. This makes our model more tolerant to appearance changes and less susceptible to model over-fitting. Extensive experiments are carried out to demonstrate that 1) by formulating the person re-identification problem as a distance learning problem, notable improvement on matching accuracy can be obtained against conventional person re-identification techniques, which is particularly significant when the training sample size is small; and 2) our PRDC outperforms not only existing distance learning methods but also alternative learning methods based on boosting and learning to rank.