Improved scene identification and object detection on egocentric vision of daily activities

This work investigates the relationship between scene and associated objects on daily activities under egocentric vision constraints. Daily activities are performed in prototypical scenes that share a lot of visual appearances independent of where or by whom the video was recorded. The intrinsic characteristics of egocentric vision suggest that the location where the activity is conducted remains consistent throughout frames. This paper shows that egocentric scene identification is improved by taking the temporal context into consideration. Moreover, since most of the objects are typically associated with particular types of scenes, we show that a generic object detection method can also be improved by re-scoring the results of the object detection method according to the scene content. We first show the case where the scene identity is explicitly predicted to improve object detection, and then we show a framework using Long Short-Term Memory (LSTM) where no labeling of the scene type is needed. We performed experiments in the Activities of Daily Living (ADL) public dataset (Pirsiavash and Ramanan,2012), which is a standard benchmark for egocentric vision.