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. |