Efficient point process inference for large-scale object detection

Trung T. Pham, Seyed Hamid Rezatofighi, Ian Reid, Tat-Jun Chin

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

11 Citations (Scopus)

Abstract

We tackle the problem of large-scale object detection in images, where the number of objects can be arbitrarily large, and can exhibit significant overlap/occlusion. A successful approach to modelling the large-scale nature of this problem has been via point process density functions which jointly encode object qualities and spatial interactions. But the corresponding optimisation problem is typically difficult or intractable, and many of the best current methods rely on Monte Carlo Markov Chain (MCMC) simulation, which converges slowly in a large solution space. We propose an efficient point process inference for largescale object detection using discrete energy minimization. In particular, we approximate the solution space by a finite set of object proposals and cast the point process density function to a corresponding energy function of binary variables whose values indicate which object proposals are accepted. We resort to the local submodular approximation (LSA) based trust-region optimisation to find the optimal solution. Furthermore we analyse the error of LSA approximation, and show how to adjust the point process energy to dramatically speed up the convergence without harming the optimality. We demonstrate the superior efficiency and accuracy of our method using a variety of large-scale object detection applications such as crowd human detection, birds, cells counting/localization.

Original languageEnglish
Title of host publicationProceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
EditorsLourdes Agapito, Tamara Berg, Jana Kosecka, Lihi Zelnik-Manor
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages2837-2845
Number of pages9
ISBN (Electronic)9781467388504
ISBN (Print)9781467388528
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventIEEE Conference on Computer Vision and Pattern Recognition 2016 - Las Vegas, United States of America
Duration: 27 Jun 201630 Jun 2016
http://cvpr2016.thecvf.com/
https://ieeexplore.ieee.org/xpl/conhome/7776647/proceeding (Proceedings)

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2016
Abbreviated titleCVPR 2016
CountryUnited States of America
CityLas Vegas
Period27/06/1630/06/16
Internet address

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