Improving object detection performance using scene contextual constraints

Alamri, F. and Pugeault, N. (2022) Improving object detection performance using scene contextual constraints. IEEE Transactions on Cognitive and Developmental Systems, 14(4), pp. 1320-1330. (doi: 10.1109/TCDS.2020.3008213)

Abstract

Contextual information, such as the co-occurrence of objects and the spatial and relative size among objects, provides rich and complex information about digital scenes. It also plays an important role in improving object detection and determining out-of-context objects. In this work, we present contextual models that leverage contextual information (16 contextual relationships are applied in this paper) to enhance the performance of two of the state-of-the-art object detectors (i.e., Faster RCNN and YOLO), which are applied as a post-processing process for most of the existing detectors, especially for refining the confidences and associated categorical labels, without refining bounding boxes. We experimentally demonstrate that our models lead to enhancement in detection performance using the most common dataset used in this field (MSCOCO), where in some experiments PASCAL2012 is also used.We also show that iterating the process of applying our contextual models also enhances the detection performance further.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Pugeault, Dr Nicolas
• Authors: Alamri, F., and Pugeault, N.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: IEEE Transactions on Cognitive and Developmental Systems
• Publisher: IEEE
• ISSN: 2379-8920
• ISSN (Online): 2379-8939
• Published Online: 09 July 2020
• Copyright Holders: Copyright © 2020 IEEE
• First Published: First published in IEEE Transactions on Cognitive and Developmental Systems 14(4): 1320-1330
• Publisher Policy: Reproduced in accordance with the publisher copyright policy