A weakly-supervised approach for discovering common objects in airport video surveillance footage

Abstract

Object detection in video is a relevant task in computer vision. Standard and current detectors are typically trained in a strongly supervised way, what requires a huge amount of labelled data. In contrast, in this paper we focus on object discovery in video sequences by using sets of unlabelled data. Thus, we present an approach based on the use of two region proposal algorithms (a pretrained Region Proposal Network and an Optical Flow Proposal) to produce regions of interest that will be grouped using a clustering algorithm. Therefore, our system does not require the collaboration of a human except for assigning human understandable labels to the discovered clusters. We evaluate our approach in a set of videos recorded at the outdoor area of an airport where the aeroplanes park to load passengers and luggage (apron area).

Our experimental results suggest that the use of an unsupervised approach is valid for automatic object discovery in video sequences, obtaining a CorLoc of 86.8 and a mAP of 0.374 compared to a CorLoc of 70.4 and mAP of 0.683 achieved by a supervised Faster R-CNN trained and tested on the same dataset.