Complex Event Recognition Using Constrained Rank Optimization

Abstract

Complex event recognition is the problem of recognizing events in long and unconstrained videos. Examples of such events are birthday party, changing a tire, making a fire …etc. In this extremely challenging task, concepts have recently shown a promising direction, where core low-level events referred to as concepts are annotated and modelled using a portion of the training data, then each complex event is described using concept scores, which are features representing the occurrence confidence for the concepts in the event. However, because of the complex nature of the videos, both the concept models and the corresponding concept scores are significantly noisy. In order to address this problem, we discuss a low-rank formulation, which combines the precisely annotated videos used to train the concepts, with the rich concept scores. The approach presented in this chapter finds a new representation for each event, which is not only low-rank, but also constrained to adhere to the concept annotation, thus suppressing the noise, and maintaining a consistent occurrence of the concepts in each event. Extensive experiments on large scale real world dataset TRECVID Multimedia Event Detection 2011 and 2012 demonstrate that the approach consistently improves the discriminativity of the concept scores by a significant margin.KeywordsComplex Event RecognitionConcept ScoresOccurrence ConfidenceUnconstrained VideosLarge-scale Real-world DatasetsThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.