Multiple Pedestrian Tracking From Monocular Videos in an Interacting Multiple Model Framework

2018 ◽  
Vol 27 (3) ◽  
pp. 1361-1375 ◽  
Author(s):  
Zhengqiang Jiang ◽  
Du Q. Huynh
Keyword(s):  
Interacting Multiple Model ◽  
Multiple Model ◽  
Pedestrian Tracking ◽  
Model Framework

scholarly journals Performance Evaluation of a Maneuver Classification Algorithm Using Different Motion Models in a Multi-Model Framework

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 347
Author(s):  
Máté Kolat ◽  
Olivér Törő ◽  
Tamás Bécsi
Keyword(s):  
Performance Evaluation ◽  
Kalman Filtering ◽  
Classification Algorithm ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Simulation Environment ◽  
Model Framework ◽  
Motion Models ◽  
Environment Perception

Environment perception is one of the major challenges in the vehicle industry nowadays, as acknowledging the intentions of the surrounding traffic participants can profoundly decrease the occurrence of accidents. Consequently, this paper focuses on comparing different motion models, acknowledging their role in the performance of maneuver classification. In particular, this paper proposes utilizing the Interacting Multiple Model framework complemented with constrained Kalman filtering in this domain that enables the comparisons of the different motions models’ accuracy. The performance of the proposed method with different motion models is thoroughly evaluated in a simulation environment, including an observer and observed vehicle.

Interacting multiple model algorithm based on joint likelihood estimation

2011 ◽  
Vol 28 (4-6) ◽  
pp. 427-432
Author(s):  
Jie Sun ◽  
Chaoshu Jiang ◽  
Zhuming Chen ◽  
Wei Zhang
Keyword(s):  
Likelihood Estimation ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Joint Likelihood ◽  
Model Algorithm
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