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Table 1 Human tracking algorithms within a camera

From: Human tracking over camera networks: a review

Method Description Typical techniques Pros Cons
Generative trackers To estimate each target’s location and correspondence through searching the most similar target candidate with the minimal reconstruction error KF Real-time tracking Subject to linear target state transition and Gaussian noise distributions;
apt to lose the tracked target when a target is occluded
PF Non-linear/non-Gaussian tracking and multi-modal processing High computational complexity
KT Real-time tracking Cannot deal with long-term total target occlusion
Discriminative trackers To separate targets from the background through a classifier, and then jointly to establish these targets’ correspondences across frames through a target association algorithm JPDAF Multi-target tracking Subject to data association between a fixed number of tracked targets
MHT Variable number of multi-target tracking under occlusion Vitally high computational requirement
FNF Variable number of multi-target tracking under occlusion Cannot effectively deal with long-time target occlusion