Multi-Bernoulli filtering for keypoint-based visual tracking
MetadataShow full item record
© 2016 IEEE.In this paper, we consider a single object visual tracking problem using multi-object filtering technique. We represent object appearance as a multi-object distribution of keypoints. Hidden positions of keypoints are observed by using SURF feature detectors and multi-Bernoulli filtering is used for tracking of keypoints. Unlike other feature matching based object trackers, multi-Bernoulli filtering based tracker is free from combinatorial matching problem. The estimated number of keypoints can be used as a quality measure to determine track re-initialization when it is necessary. Experimental results show that multi-object filtering can be one of effective solutions for single object visual tracking.
Showing items related by title, author, creator and subject.
Mallick, M.; Rubin, S.; Vo, Ba-Ngu (2013)Space object (satellite or space-debris) tracking (SOT) has not received much attention in the Information Fusion community, although the first Fusion conference was held in 1998. A special session on SOT was organized ...
Jones, B.; Vo, Ba Tuong; Vo, Ba-Ngu (2016)Space-object tracking systems require robust and accurate methods of multi-target state estimation and prediction. This paper presents the application of labeled multi-Bernoulli filters for space-object tracking, and ...
Reuter, S.; Vo, Ba Tuong; Vo, Ba-Ngu; Dietmayer, K. (2014)In this paper, we propose the labeled multi-Bernoulli filter which explicitly estimates target tracks and provides a more accurate approximation of the multi-object Bayes update than the multi-Bernoulli filter. In particular, ...