  In this paper, a simple yet efficient activity recognition method for
first-person video is introduced. The proposed method is appropriate for
representation of high-dimensional features such as those extracted from
convolutional neural networks (CNNs). The per-frame (per-segment) extracted
features are considered as a set of time series, and inter and intra-time
series relations are employed to represent the video descriptors. To find the
inter-time relations, the series are grouped and the linear correlation between
each pair of groups is calculated. The relations between them can represent the
scene dynamics and local motions. The introduced grouping strategy helps to
considerably reduce the computational cost. Furthermore, we split the series in
temporal direction in order to preserve long term motions and better focus on
each local time window. In order to extract the cyclic motion patterns, which
can be considered as primary components of various activities, intra-time
series correlations are exploited. The representation method results in highly
discriminative features which can be linearly classified. The experiments
confirm that our method outperforms the state-of-the-art methods on recognizing
first-person activities on the two challenging first-person datasets.
