  We focus on first-person action recognition from egocentric videos. Unlike
third person domain, researchers have divided first-person actions into two
categories: involving hand-object interactions and the ones without, and
developed separate techniques for the two action categories. Further, it has
been argued that traditional cues used for third person action recognition do
not suffice, and egocentric specific features, such as head motion and handled
objects have been used for such actions. Unlike the state-of-the-art
approaches, we show that a regular two stream Convolutional Neural Network
(CNN) with Long Short-Term Memory (LSTM) architecture, having separate streams
for objects and motion, can generalize to all categories of first-person
actions. The proposed approach unifies the feature learned by all action
categories, making the proposed architecture much more practical. In an
important observation, we note that the size of the objects visible in the
egocentric videos is much smaller. We show that the performance of the proposed
model improves after cropping and resizing frames to make the size of objects
comparable to the size of ImageNet's objects. Our experiments on the standard
datasets: GTEA, EGTEA Gaze+, HUJI, ADL, UTE, and Kitchen, proves that our model
significantly outperforms various state-of-the-art techniques.
