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Table 4 Human tracking algorithms across non-overlapping cameras

From: Human tracking over camera networks: a review

Method Description Typical technique Pros Cons
Human re-id To identify whether a human taken from one camera is the same as one taken from another camera or not Feature extraction Extracting discriminative and robust visual features help to improve human re-id accuracy Difficult to find suitable feature combination to effectively describe human appearance
Distance metric learning Learning a distance metric helps to mitigate cross-view human appearances’ variations. Require manually pairwise labeling of training data
CLM-based tracking To track humans through establishing the link (correlation) models between two adjacent or among multiple neighboring cameras Supervised learning-based CLM Easy to establish and learn CLM Unfeasible to scale up to large-scale camera networks due to a mass of manually labeled efforts
Unsupervised learning-based CLM Help to achieve self-organized and scalable large-scale camera networks due to no need of human labeling efforts Estimated CLM may decrease the accuracy due to higher outlier percentage.
GM-based tracking To track humans through partite graph matching based on input observations (detections, tracklets, trajectories, or pairs) MAP optimization solution framework Human tracking in complex scenes such as occlusion, crowd, and interference of appearance similarity It is difficult to get the optimal solution.