%0 Conference Proceedings
%T Joint Multi-object Detection and Segmentation from an Untrimmed Video
%+ Xi'an Jiaotong University (Xjtu)
%+ HERE technologies
%+ Wormpex AI Research
%A Liu, Xinling
%A Wang, Le
%A Zhang, Qilin
%A Zheng, Nanning
%A Hua, Gang
%Z Part 6: Object Tracking/Object Detection Systems
%< avec comité de lecture
%@ 978-3-030-49160-4
%( Artificial Intelligence Applications and Innovations
%B 16th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)
%C Neos Marmaras, Greece
%Y Ilias Maglogiannis
%Y Lazaros Iliadis
%Y Elias Pimenidis
%I Springer International Publishing
%3 IFIP Advances in Information and Communication Technology
%V AICT-583
%N Part I
%P 317-329
%8 2020-06-05
%D 2020
%R 10.1007/978-3-030-49161-1_27
%K Video object segmentation
%K Data association
%K Object detection
%Z Computer Science [cs]Conference papers
%X In this paper, we present a novel method for jointly detecting and segmenting multiple objects from an untrimmed video. Unlike most existing video object segmentation methods that can only handle a trimmed video in which all video frames contain the target objects, we address a more practical and difficult problem, i.e., joint multi-object detection and segmentation from an untrimmed video where the target objects do not always appear per frame. In particular, our method consists of two modules, i.e., object decision module and object segmentation module. The object decision module is used to detect the objects and decide which target objects need to be separated out from video. As there are usually two or more target objects and they do not always appear in the whole video, we introduce the data association into object decision module to identify their correspondences among frames. The object segmentation module aims to separate the target objects identified by object decision module. In order to extensively evaluate the proposed method, we introduce a new dataset named UNVOSeg dataset, in which $$7.2\%$$ of the video frames do not contain objects. Experimental results on four datasets demonstrate that our method outperforms most of the state-of-the-art approaches.
%G English
%Z TC 12
%Z WG 12.5
%2 https://inria.hal.science/hal-04050585/document
%2 https://inria.hal.science/hal-04050585/file/497040_1_En_27_Chapter.pdf
%L hal-04050585
%U https://inria.hal.science/hal-04050585
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-AIAI
%~ IFIP-WG12-5
%~ IFIP-AICT-583