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Table 3 Qualitative comparison of discriminative trackers-based human tracking within a camera

From: Human tracking over camera networks: a review

Item No. Used discriminative trackers Speed Occlusion Scale change Shape deformation
1 JLF-based JPDAF (Rasmussen et al. [18]) Low
2 Sample-based JPDAF (Schulz et al. [19]) Low
3 Clustering-based JPDAF (Naqvi et al. [20]) Low
4 JPDAF revisited (Rezatofighi et al. [21]) Moderate
5 Reliability measure-driven MHT (Zúñiga et al. [22]) High
6 MHT revisited (Kim et al. [23]) Moderate
7 Multiple association-based MHT (Joo et al. [24]) High
8 Hierarchical MHT (Zulkifley et al. [25]) Low
9 EOM-based FNF (Zhang et al. [26]) High
10 Greedy algorithms-based FNF (Pirsiavash et al. [27]) High
11 Lagrangian relaxation-based FNF (Butt et al. [28]) High
12 Multi-way data association-based FNF (Wu et al. [29]) Low
  1. Symbol √ means that the used discriminative trackers-based human tracking within a camera can deal with the situations of occlusion, scale change, and shape deformation