Track-Clustering Error Evaluation for Track-Based Multi-camera Tracking System Employing Human Re-identification
Journal
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Journal Volume
2017-July
Pages
1416-1424
Date Issued
2017
Author(s)
Abstract
In this study, we present a set of new evaluation measures for the track-based multi-camera tracking (T-MCT) task leveraging the clustering measurements. We demonstrate that the proposed evaluation measures provide notable advantages over previous ones. Moreover, a distributed and online T-MCT framework is proposed, where re-identification (Re-id) is embedded in T-MCT, to confirm the validity of the proposed evaluation measures. Experimental results reveal that with the proposed evaluation measures, the performance of T-MCT can be accurately measured, which is highly correlated to the performance of Re-id. Furthermore, it is also noted that our T-MCT framework achieves competitive score on the DukeMTMC dataset when compared to the previous work that used global optimization algorithms. Both the evaluation measures and the inter-camera tracking framework are proven to be the stepping stone for multi-camera tracking.
Type
conference paper