Structuring Continuous Video Recordings of Everyday Life Using Time-Constrained Clustering

As personal wearable devices become more powerful and ubiquitous, soon everyone will be capable to continuously record video of everyday life. The archive of continuous recordings need to be segmented into manageable units so that they can be efficiently browsed and indexed by any video retrieval systems. Many researchers approach the problem in two-pass methods: segmenting the continuous recordings into chunks, followed by clustering chunks. In this paper we propose a novel one-pass algorithm to accomplish both tasks at the same time by imposing time constraints on the K-Means clustering algorithm. We evaluate the proposed algorithm on 62.5 hours of continuous recordings, and the experiment results show that time-constrained clustering algorithm substantially outperforms the unconstrained version.