Adaptive object tracking by learning background context

One challenge when tracking objects is to adapt the object representation depending on the scene context to account for changes in illumination, coloring, scaling, etc. Here, we present a solution that is based on our earlier approach for object tracking using particle filters and component-based descriptors. We extend the approach to deal with changing backgrounds by using a quick training phase with user interaction at the beginning of an image sequence. During this phase, some background clusters are learned along with object representations for those clusters. Next, for the rest of the sequence the best fitting background cluster is determined for each frame and the corresponding object representation is used for tracking. Experiments show a particle filter adapting to background changes can efficiently track objects and persons in natural scenes and results in higher tracking results than the basic approach. Additionally, using an object tracker to follow the main character i...

• Type: Journal
• Year: 2012
• Where: CVPR
• Authors: Ali Borji, Simone Frintrop, Dicky N. Sihite, Laurent Itti
• Keywords: Computer Vision, Cvpr 2012, Object Representation, Object Representations, Objects And Persons