Collaborative detection of repetitive behavior by multiple uncalibrated cameras

In smart environments, the embedded sensing systems should intelligently adapt to the behavior of the users. Many interesting types of behavior are characterized by repetition of actions such as certain activities or movements. A generic methodology to detect and classify repetitions that may occur at different scales is introduced in this paper. The proposed method is called Action History Matrices (AHM). The properties of AHM for detecting repetitive movement behavior are demonstrated in analyzing four customer behavior classes in a shop environment observed by multiple uncalibrated cameras.Two different datasets, video recordings in the shop environment and motion path simulations, are created and used in the experiments. The AHM-based system achieves an accuracy of 97% with most suitable scale and naive Bayesian classifier on the single-view simulated movement data. In addition, the performance of two fusion levels and three fusion methods are compared with AHM method on the multi-view recordings. In our results, fusion at the decision-level offers consistently better accuracy than feature-level, and the coverage-based view-selection fusion method (51%) marginally outperforms the majority method. The upper limit with the recorded data for accuracy by view-selection is found to be 75%.