  This work presents a retrieval pipeline and evaluation scheme for the problem
of finding the last appearance of personal objects in a large dataset of images
captured from a wearable camera. Each personal object is modelled by a small
set of images that define a query for a visual search engine.The retrieved
results are reranked considering the temporal timestamps of the images to
increase the relevance of the later detections. Finally, a temporal
interleaving of the results is introduced for robustness against false
detections. The Mean Reciprocal Rank is proposed as a metric to evaluate this
problem. This application could help into developing personal assistants
capable of helping users when they do not remember where they left their
personal belongings.
