  A video can be represented as a sequence of tracklets, each spanning 10-20
frames, and associated with one entity (eg. a person). The task of \emph{Entity
Discovery} in videos can be naturally posed as tracklet clustering. We approach
this task by leveraging \emph{Temporal Coherence}(TC): the fundamental property
of videos that each tracklet is likely to be associated with the same entity as
its temporal neighbors. Our major contributions are the first Bayesian
nonparametric models for TC at tracklet-level. We extend Chinese Restaurant
Process (CRP) to propose TC-CRP, and further to Temporally Coherent Chinese
Restaurant Franchise (TC-CRF) to jointly model short temporal segments. On the
task of discovering persons in TV serial videos without meta-data like scripts,
these methods show considerable improvement in cluster purity and person
coverage compared to state-of-the-art approaches to tracklet clustering. We
represent entities with mixture components, and tracklets with vectors of very
generic features, which can work for any type of entity (not necessarily
person). The proposed methods can perform online tracklet clustering on
streaming videos with little performance deterioration unlike existing
approaches, and can automatically reject tracklets resulting from false
detections. Finally we discuss entity-driven video summarization- where some
temporal segments of the video are selected automatically based on the
discovered entities.
