Evolving networks for group object motion estimation

This paper proposes a technique for group object motion estimation based on evolving graph networks. The main novelty over alternative group tracking techniques stems from learning the network structure for the group. An algorithm is proposed for automatic graph structure initialisation, incorporati...

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Veröffentlicht in:IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications S. 97 - 106
Hauptverfasser: Gning, A, Mihaylova, L, Maskell, S, Sze Kim Pang, Godsill, S
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: Stevenage IET 2008
Schlagworte:
Combinatorial mathematics
Computer vision and image processing techniques
Monte Carlo methods
Optical, image and video signal processing
evolving graph network model
Monte Carlo methods
sequential Monte Carlo framework
graph theory
motion estimation
object detection
group object motion estimation
tracking
group object tracking technique
automatic graph structure initialisation
ISBN:0863419100, 9780863419102
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Beschreibung
Zusammenfassung:This paper proposes a technique for group object motion estimation based on evolving graph networks. The main novelty over alternative group tracking techniques stems from learning the network structure for the group. An algorithm is proposed for automatic graph structure initialisation, incorporation of new nodes and unexisting nodes removal in parallel with the edge update. This evolving graph model is combined with the sequential Monte Carlo framework and its effectiveness is illustrated over a complex scenario for group motion estimation in urban environment. Results with merging, splitting and crossing of the groups are presented with high estimation accuracy.
ISBN:0863419100
9780863419102
DOI:10.1049/ic:20080061