A Correlation Based Feature Representation for First-Person Activity Recognition
This work addresses activity recognition in first-person video, an incremental improvement for applications like wearable cameras and robotics.
The authors tackled the problem of first-person activity recognition by introducing a correlation-based feature representation method that groups and correlates time series of CNN features to capture scene dynamics and cyclic motion patterns, resulting in state-of-the-art performance on two challenging datasets.
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.