Person Re-Identification for Improved Multi-Person Multi-Camera Tracking by Continuous Entity Association

Neeti Narayan, Nishant Sankaran, Devansh Arpit, Karthik Dantu, Srirangaraj Setlur, Venu Govindaraju; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 64-70

Abstract


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.

Related Material


[pdf]
[bibtex]
@InProceedings{Narayan_2017_CVPR_Workshops,
author = {Narayan, Neeti and Sankaran, Nishant and Arpit, Devansh and Dantu, Karthik and Setlur, Srirangaraj and Govindaraju, Venu},
title = {Person Re-Identification for Improved Multi-Person Multi-Camera Tracking by Continuous Entity Association},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}
}