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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Data fusion for unsupervised video object detection, tracking and geo-positioning
AU - Kolev, Denis Georgiev
AU - Markarian, Garegin
AU - Kangin, Dmitry
PY - 2015/7/4
Y1 - 2015/7/4
N2 - In this work we describe a system and propose a novel algorithm for moving object detection and tracking based on video feed. Apart of many well-known algorithms, it performs detection in unsupervised style, using velocity criteria for the objects detection. The algorithm utilises data from a single camera and Inertial Measurement Unit (IMU) sensors and performs fusion of video and sensory data captured from the UAV. The algorithm includes object tracking and detection, augmented by object geographical co-ordinates estimation. The algorithm can be generalised for any particular video sensor and is not restricted to any specific applications. For object tracking, Bayesian filter scheme combined with approximate inference is utilised. Object localisation in real-world co-ordinates is based on the tracking results and IMU sensor measurements.
AB - In this work we describe a system and propose a novel algorithm for moving object detection and tracking based on video feed. Apart of many well-known algorithms, it performs detection in unsupervised style, using velocity criteria for the objects detection. The algorithm utilises data from a single camera and Inertial Measurement Unit (IMU) sensors and performs fusion of video and sensory data captured from the UAV. The algorithm includes object tracking and detection, augmented by object geographical co-ordinates estimation. The algorithm can be generalised for any particular video sensor and is not restricted to any specific applications. For object tracking, Bayesian filter scheme combined with approximate inference is utilised. Object localisation in real-world co-ordinates is based on the tracking results and IMU sensor measurements.
KW - Bayesian filters
KW - UAV
KW - object tracking
KW - unsupervised detection
KW - rigid motion segmentation
M3 - Conference contribution/Paper
SN - 9781479974047
SP - 142
EP - 149
BT - Information Fusion (Fusion), 2015 18th International Conference on
PB - IEEE
T2 - International Conference on Information Fusion'2015
Y2 - 4 July 2015 through 8 July 2015
ER -