Person Re-identification for Improved Multi-person Multi-camera Tracking by Continuous Entity Association
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)(2017)
摘要
We present a novel approach to person tracking within the context of entity association. In large-scale distributed multi-camera systems, person re-identification is a challenging computer vision task as the problem is two-fold: detecting entities through identification and recognition techniques; and connecting entities temporally by associating them in often crowded environments. Since tracking essentially involves linking detections, we can reformulate it purely as a re-identification task. The inherent advantage of such a reformulation lies in the ability of the tracking algorithm to effectively handle temporal discontinuities in multi-camera environments. To accomplish this, we model human appearance, face biometric and location constraints across cameras. We do not make restrictive assumptions such as number of people in a scene. Our approach is validated by using a simple and efficient inference algorithm. Results on two publicly available datasets, CamNeT and DukeMTMC, are significantly better compared to other existing methods.
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关键词
person re-identification,multiperson multicamera tracking improvement,continuous entity association,large-scale distributed multicamera systems,computer vision task,multicamera environments,face biometric,location constraints,CamNeT,DukeMTMC
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