Paper Info
Title
Adaptive Spatio-temporal Model Based Multiple Object Tracking in Video Sequences Considering a Moving Camera
Abstract
Tracking multiple objects in a moving camera is challenging. Due to the irregular movements of the camera, the displacement, scale, and appearance of the objects can be difficult to predict and track. To cope with these problems, we propose an Adaptive Apatio-temporal (AST) model, which explicitly estimate the movement and scale of targets in the view of the moving camera. Moreover, the interactions among objects are also considered to increase the robustness. We introduce our model to the multiple hypothesis tracking and achieve a competitive result on the public benchmark, which includes video of both moving and statistic camera.
Year
DOI
Venue
2018
10.1109/UV.2018.8642156
2018 4th International Conference on Universal Village (UV)
Keywords
Field
DocType
multiple object tracking,multiple hypothesis tracking,moving camera,adaptive spatio-temporal model,tracking-by-detection
Computer vision,Multiple hypothesis tracking,Statistic,Computer science,Robustness (computer science),Video tracking,Artificial intelligence
Conference
ISBN
Citations 
PageRank 
978-1-5386-5197-1
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Yi Tao100.34
Jiahui Chen24412.26
Yajun Fang33110.61
Ichiro Masaki401.01
berthold k p horn53618.55