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Avoiding over-detection: Towards combined object detection and counting

Lookup NU author(s): Phillip Jackson, Professor Boguslaw ObaraORCiD



This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by Springer Verlag, 2017.

For re-use rights please refer to the publisher's terms and conditions.


© Springer International Publishing AG 2017. Existing object detection frameworks in the deep learning field generally over-detect objects, and use non-maximum suppression (NMS) to filter out excess detections, leaving one bounding box per object. This works well so long as the ground-truth bounding boxes do not overlap heavily, as would be the case with objects that partially occlude each other, or are packed densely together. In these cases it would be beneficial, and more elegant, to have a fully end-to-end system that outputs the correct number of objects without requiring a separate NMS stage. In this paper we discuss the challenges involved in solving this problem, and demonstrate preliminary results from a prototype system.

Publication metadata

Author(s): Jackson PTG, Obara B

Editor(s): Leszek Rutkowski, Marcin Korytkowski, Rafał Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 16th International Conference on Artificial Intelligence and Soft Computing (ICAISC 2017)

Year of Conference: 2017

Pages: 75-85

Online publication date: 27/05/2017

Acceptance date: 02/04/2016

Date deposited: 04/05/2021

ISSN: 0302-9743

Publisher: Springer Verlag


DOI: 10.1007/978-3-319-59063-9_7

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science

ISBN: 9783319590622