Improving egocentric vision of daily activities

In this paper, we investigates the interplay between scene and objects on daily activities under egocentric vision constraints. The nature of egocentric vision implies that the identity of the current scene remains consistent for several frames. We showed that this constraint can be used to improve several scene identification baselines including the current state of the art scene identification method. We also show that the scene identity can be used to improve the object detection. In generic object detection, models for objects typically only considers local context, ignoring the global scene context; however in daily activities, objects are typically associated to particular types of scenes. We exploited this context clue to re-score the object detectors. Re-scoring function is learned from scene classifiers and object detectors in a validation set. In testing time, models of objects are weighted according to the scene identity score (context) of the tested frame, improving the object detection as measured by mAP, respect to object detectors without the scene identity clue. Our experiments were performed in the Activities of Daily Living (ADL) public dataset [1] which is a standard benchmark for egocentric vision.