Prioritizing the Propagation of Identity Beliefs for Multi-object Tracking
(2012) British Machine Vision Conference — Location: University of Surrey, Guildford (3.September.2012)
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- Abstract
- Multi-object tracking requires locating the targets as well as labeling their identities. Inferring identities of the targets from their appearances is a challenge when the avail- ability and the reliability of the observation process do vary along the time and space. The purpose of this paper is to assign identities to those appearance measurements using a graph-based formalism. Each node of the graph corresponds to a tracklet, which is defined to be a sequence of positions that very likely correspond to the same physical target. Tracklets are pre-computed and our work investigates how to assign them identities, knowing the reference appearance of each target. Initially, each node is assigned a probability distribution over the set of possible identities, based on the observed appearance features. Afterwards, belief propagation is considered to infer the identities of more ambiguous nodes from those of less ambiguous nodes, by exploiting the graph constraints and the measures of similarities between the nodes. In contrast to the standard belief propagation, which treats the nodes in an arbitrary order, the pro- posed method uses a priority-based belief propagation, in which less ambiguous nodes are scheduled to transmit their messages first. Validation is performed on a real-life basketball dataset. The proposed method achieves 89% identification rate, which is an improvement of 21% and 16% compared to individ- ual identity assignment, and to standard belief propagation, respectively.
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K.C., A. K., & De Vleeschouwer, C. (2012). Prioritizing the Propagation of Identity Beliefs for Multi-object Tracking. British Machine Vision Conference 2012, Surrey, UK. Published. British Machine Vision Conference, University of Surrey, Guildford. https://doi.org/10.5244/C.26.117