scholarly journals Introductory Chapter: Kalman Filter - The Working Horse of Object Tracking Systems Nowadays

2019 ◽  
Author(s):  
Felix Govaers
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Tracking Systems

scholarly journals Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2841
Author(s):  
Khizer Mehmood ◽  
Abdul Jalil ◽  
Ahmad Ali ◽  
Baber Khan ◽  
Maria Murad ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Environmental Changes ◽  
Correlation Filter ◽  
Temporal Context ◽  
Visual Object ◽  
Target Model ◽  
Change Of Scale ◽  
Tracking Model ◽  
Spatio Temporal

Despite eminent progress in recent years, various challenges associated with object tracking algorithms such as scale variations, partial or full occlusions, background clutters, illumination variations are still required to be resolved with improved estimation for real-time applications. This paper proposes a robust and fast algorithm for object tracking based on spatio-temporal context (STC). A pyramid representation-based scale correlation filter is incorporated to overcome the STC’s inability on the rapid change of scale of target. It learns appearance induced by variations in the target scale sampled at a different set of scales. During occlusion, most correlation filter trackers start drifting due to the wrong update of samples. To prevent the target model from drift, an occlusion detection and handling mechanism are incorporated. Occlusion is detected from the peak correlation score of the response map. It continuously predicts target location during occlusion and passes it to the STC tracking model. After the successful detection of occlusion, an extended Kalman filter is used for occlusion handling. This decreases the chance of tracking failure as the Kalman filter continuously updates itself and the tracking model. Further improvement to the model is provided by fusion with average peak to correlation energy (APCE) criteria, which automatically update the target model to deal with environmental changes. Extensive calculations on the benchmark datasets indicate the efficacy of the proposed tracking method with state of the art in terms of performance analysis.

scholarly journals Kalman filter models of multiple-object tracking within an attentional window

2014 ◽  
Vol 14 (10) ◽  
pp. 706-706
Author(s):  
S.-h. Zhong ◽  
Z. Ma ◽  
C. Wilson ◽  
J. Flombaum
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Multiple Object Tracking ◽  
Multiple Object ◽  
Attentional Window

scholarly journals Optimized object tracking technique using Kalman filter

2016 ◽  
Vol 7 (1) ◽  
pp. 57 ◽  
Author(s):  
Liana Ellen Taylor ◽  
Midriem Mirdanies ◽  
Roni Permana Saputra
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Tracking Technique
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