The variability of the illumination and the correlation of the object

the robustness of multi-object tracking algorithm is relatively poor because of the complexity of the background.The main challenges in multi-object tracking include occlusion

false positives

complexity of object motion

and ambiguity caused by similar features in appearance.To solve the above problems

a hierarchical multi-object tracking algorithm based on globally multiple maximum clique graphs is proposed. The method is based on global association of data association

hierarchical

and network flow theory and uses a two-layer framework.Each layer uses a shorter trajectory to form a longer trajectory.An undirected graph is first constructed according to the network flow theory.The nodes are composed of several track segments and the weights are obtained using the linear combination of the motion model and the appearance model of the object.Then

the occlusion object is processed by the aggregation dummy node

and the spatial constraint is added to solve the problem of identity transformation.Finally

the mixed-binary-integer programming is used to solve the undirected graphs on the superposition problem.Simultaneously

a plurality of maximal cliques is obtained. Experiments are conducted on public datasets through TUD-Stadmitte

TUD-Crossing

PETS2009

Parking Lot 1

Parking Lot 2

and Town Center to verify the method

and all the datasets show desirable results.The number of identity transformation handled by the dataset Town Center is 12

and is higher by more than 5% for the dataset TUD-Stadmitte. Based on the idea of data association

this paper proposes a hierarchical multi-object tracking algorithm based on globally multiple maximum clique graphs.The spatial constraint of key can effectively deal with multi-object tracking problem

especially the problem of occlusion effect.This method has practical application value in intelligent video surveillance.