Object Detection in unmanned vehicle with End-to End Edge-Enhanced GAN and Object Detector Network

Zhang, Shuangjian; Song, Yong

doi:10.1109/ccdc52312.2021.9601908

Cited by 1 publication

(2 citation statements)

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“…Detection Algorithm. In this paper, we propose a you only look once (YOLO)-based multitarget correlation target detection algorithm, which mainly solves the difficult problem of pedestrian target detection in the dense places [25,26]. Adding target features can increase the difference in appearance features when the target appearance is similar.…”

Section: You Only Look Once-based Multitarget Correlationmentioning

confidence: 99%

“…In order to verify the effectiveness of this algorithm, we use OTB2015 by Gao et al [22] and IVOT2018 by Ali et al [23] datasets as validation sets and compare them with several classical tracking algorithms, based on the various experimental analyses, we can see that this algorithm has excellent performance. [24], SiamDW, SiamRPN, SRDCF [25], fDSST [27], Staple [28], and representative trackers of SiamFC.…”

Section: Experiments and Analysismentioning

confidence: 99%

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Generative Target Tracking Method with Improved Generative Adversarial Network

Yang,

Chen

2023

IET Circuits, Devices & Systems

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Multitarget tracking is prone to target loss, identity exchange, and jumping problems in the context of complex background, target occlusion, target scale, and pose transformation. In this paper, we proposed a target tracking algorithm based on the conditional adversarial generative twin networks, using the improved you only look once multitarget association algorithm to classify and detect the position of the target to be detected in the current frame, constructing a feature extraction model using generative adversarial networks (GANs) to learn the main features and subtle features of the target, and then using GANs to generate the motion trajectories of multiple targets, finally fuzing the motion and appearance information of the target to obtain the optimal match. The optimal matching of the tracked targets is obtained. The experimental results under OTB2015 and IVOT2018 datasets demonstrate that the proposed multitarget tracking algorithm has high accuracy and robustness, with 65% less jumps and 0.25% more accuracy than the current algorithms with minimal identity exchange and jumps.

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Section: You Only Look Once-based Multitarget Correlationmentioning

confidence: 99%