Complex event recognition by latent temporal models of concepts

Complex event recognition is an expanding research area aiming to recognize entities of high-level semantics in videos. Typical approaches exploit the so-called “bags” of spatiotemporal features such as STIP, ISA and DTF-HOG; yet, more recently, the notion of concept has emerged as an alternative, intermediate representation with greater descriptive power, and “bags of concepts” have been used for recognition. In this paper we argue that concepts in an event tend to articulate over a discernible temporal structure and we exploit a temporal model using the scores of concept detectors as measurements. In addition, we propose several heuristics to improve the initialization of the model's latent states and take advantage of the time-sparsity of the concepts. Experimental results on videos from the challenging TRECVID MED 2012 dataset show that the proposed approach achieves an improvement in average precision of 8.92% over comparable bags of concepts, thus validating the use of temporal structure over concepts for complex event recognition.