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Sensors 2017, 17(4), 883;

Multiple Objects Fusion Tracker Using a Matching Network for Adaptively Represented Instance Pairs

Department of Media Engineering, Catholic University of Korea, 43-1, Yeoggok 2-dong, Wonmmi-gu, Bucheon-si, Gyeonggi-do 14662, Korea
Author to whom correspondence should be addressed.
Academic Editor: Simon X. Yang
Received: 27 February 2017 / Revised: 14 April 2017 / Accepted: 14 April 2017 / Published: 18 April 2017
(This article belongs to the Special Issue Sensors for Transportation)
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Multiple-object tracking is affected by various sources of distortion, such as occlusion, illumination variations and motion changes. Overcoming these distortions by tracking on RGB frames, such as shifting, has limitations because of material distortions caused by RGB frames. To overcome these distortions, we propose a multiple-object fusion tracker (MOFT), which uses a combination of 3D point clouds and corresponding RGB frames. The MOFT uses a matching function initialized on large-scale external sequences to determine which candidates in the current frame match with the target object in the previous frame. After conducting tracking on a few frames, the initialized matching function is fine-tuned according to the appearance models of target objects. The fine-tuning process of the matching function is constructed as a structured form with diverse matching function branches. In general multiple object tracking situations, scale variations for a scene occur depending on the distance between the target objects and the sensors. If the target objects in various scales are equally represented with the same strategy, information losses will occur for any representation of the target objects. In this paper, the output map of the convolutional layer obtained from a pre-trained convolutional neural network is used to adaptively represent instances without information loss. In addition, MOFT fuses the tracking results obtained from each modality at the decision level to compensate the tracking failures of each modality using basic belief assignment, rather than fusing modalities by selectively using the features of each modality. Experimental results indicate that the proposed tracker provides state-of-the-art performance considering multiple objects tracking (MOT) and KITTIbenchmarks. View Full-Text
Keywords: multiple objects tracking; deep learning; multiple sensor fusion; LIDAR; CCD multiple objects tracking; deep learning; multiple sensor fusion; LIDAR; CCD

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Oh, S.-I.; Kang, H.-B. Multiple Objects Fusion Tracker Using a Matching Network for Adaptively Represented Instance Pairs. Sensors 2017, 17, 883.

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