Object Detection-Based Location and Activity Classification from Egocentric Videos: A Systematic Analysis

Abstract

Egocentric vision has emerged in the daily practice of application domains such as lifelogging, activity monitoring, robot navigation and the analysis of social interactions. Plenty of research focuses on location detection and activity recognition, with applications in the area of Ambient Assisted Living. The basis of this work is the idea that indoor locations and daily activities can be characterized by the presence of specific objects. Objects can be obtained either from laborious human annotations or automatically, using vision-based detectors. We perform a study regarding the use of object detections as input for location and activity classification and analyze the influence of various detection parameters. We compare our detections against manually provided object labels and show that location classification is affected by detection quality and quantity. Utilization of the temporal structure in object detections mitigates the consequences of noisy ones. Moreover, we determine that the recognition of activities is related to the presence of specific objects and that the lack of explicit associations between certain activities and objects hurts classification performance for these activities. Finally, we discuss the outcomes of each task and our method’s potential for real-world applications.