Time based Activity Inference using Latent Dirichlet Allocation
Tanveer Afzal Faruquie, Prem Kumar Kalra, Subhashis Banerjee · 2009
In this paper we address the problem of time based activity inference in unsupervised manner for an area under surveillance. We use a Latent Dirichlet Allocation based model that captures the activities and how they change over time. We use agglomerative cluster-ing on optical flow vectors to code direction and spatial information. In this model each activity is associated with not only a mixture distribution over these cluster occurrences but also on the distribution over timestamps of their occurrences. Our method thus helps in determining the prominence and the correlation of activities over a period of time. 1