Event recognition in egocentric videos using a novel trajectory based feature

This paper proposes an approach for event recognition in Egocentric videos using dense trajectories over Gradient Flow - Space Time Interest Point (GF-STIP) feature. We focus on recognizing events of diverse categories (including indoor and outdoor activities, sports and social activities and adventures) in egocentric videos. We introduce a dataset with diverse egocentric events, as all the existing egocentric activity recognition datasets consist of indoor videos only. The dataset introduced in this paper contains 102 videos with 9 different events (containing indoor and outdoor videos with varying lighting conditions). We extract Space Time Interest Points (STIP) from each frame of the video. The interest points are taken as the lead pixels and Gradient-Weighted Optical Flow (GWOF) features are calculated on the lead pixels by multiplying the optical flow measure and the magnitude of gradient at the pixel, to obtain the GF-STIP feature. We construct pose descriptors with the GF-STIP feature. We use the GF-STIP descriptors for recognizing events in egocentric videos with three different approaches: following a Bag of Words (BoW) model, implementing Fisher Vectors and obtaining dense trajectories for the videos. We show that the dense trajectory features based on the proposed GF-STIP descriptors enhance the efficacy of the event recognition system in egocentric videos.