Conditional GAN based individual and global motion fusion for multiple object tracking in UAV videos

Yu, Hongyang; Li, Guorong; Su, Li; Zhong, Bineng; Yao, Hongxun; Huang, Qingming

doi:10.1016/j.patrec.2019.12.018

Cited by 23 publications

(12 citation statements)

References 9 publications

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“…Tracking-by-Detection (TBD) is the mainstream method of MOT [147], [159], [164]- [167], [167], [168]. The main steps of TBDs are to first detect all objects of interests for the current frame, and then perform data associated with the previous frame for tracking.…”

Section: A Tracking-by-detectionmentioning

confidence: 99%

“…Tools.Appl. 2019 -Tracktor++ [158] Occlusions, crowded scenes VisDrone-MOT ICCV 2019 https://git.io/fjQr8 IPGAT [159] Small object, appearance unreliable UAVDT, Stanford Drone PRL 2020 -Kapania et al [160] Real-time VisDrone-MOT AIMS 2020 -PAS tracker [161] False detections VisDrone-MOT ECCV 2020 -DAN [77] Dense distribution, small object VisDrone-MOT NCC 2020 -DQN [162] Small target UAVDT Electronics 2021 -Cas RCNN+ FPN [80] Complex background VisDrone-MOT Transp. Res.…”

Section: A Tracking-by-detectionmentioning

confidence: 99%

“…After designing and analyzing each gate function in LSTM, Kim et al [197] proposed a novel RNN model called bilinear LSTM based on multiplication so as to improve the learning ability of longterm appearance models. Yu et al [159] estimated the individual motion and global motion by LSTM and Siamese network.…”

Section: Multiple Object Tracking Based On Memory Networkmentioning

confidence: 99%

“…OSIM [150] YOLOv3+Kalman filtering 2720 × 1530 Workstation(NVIDIA GeForce GTX 1080 Ti/-) 30 2019 +deep appearance feature Workstation(Intel UHD Graphics 630/-) Self-balance [157] LSTM 1080 × 540 Workstation(NVIDIA Titan X/32GB) -2019 Flow-tracker [151] Optical Flownet+IOU -Workstation(NVIDIA GTX 1080Ti/-) 5 2019 HMTT [153] CenterNet+IOU+OSNet ---2019 Yang et al [154] YOLOv3 + dense-trajectory-Voting 1920 × 1080 Workstation(NVIDIA GeForce GTX1080Ti/6GB) 8.6 2019 GGD [155] Faster RCNN+GGD -Workstation(NVIDIA GTX 1080/-) -2019 COMET [156] ResNet-50+Two-stream network 1080 × 540 Workstation(NVIDIA Tesla V100/16GB) 24 2019 Abughalieh et al [117] FAST 320 × 240 Laptop(Core i7-2670QM/6GB) 26.3 2019 Abughalieh et al [117] FAST 320 × 240 Embedded(Raspberry Pi 2/1GB) 10.8 2019 IPGAT [159] SiameseNet+LSTM+CGAN -Workstation(NVIDIA Titan X/32GB) -2020 Kapania et al [160] YOLOv3+RetinaNet ---2020 PAS tracker [161] Cascade R-CNN+Similarity ---2020 DAN [77] RetinaNet+DeepSORT 1500 × 1000 -2020 DQN [162] Faster R-CNN ---2021 Youssef et al [80] Cascade visible-thermal infrared cameras equipped with drones. In label terms, the bounding box is not limited to horizontal bounding box strongly dependent on robustness to direction, even have oriented bounding box, e.g., in the Vehicle Dataset.…”

Section: Referencementioning

confidence: 99%

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Deep Learning for UAV-based Object Detection and Tracking: A Survey

Wu,

Li,

Hong

et al. 2021

Preprint

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This is the pre-acceptance version, to read the final version please go to IEEE Geoscience and Remote Sensing Magazine on IEEE Xplore. Owing to effective and flexible data acquisition, unmanned aerial vehicle (UAV) has recently become a hotspot across the fields of computer vision (CV) and remote sensing (RS). Inspired by recent success of deep learning (DL), many advanced object detection and tracking approaches have been widely applied to various UAV-related tasks, such as environmental monitoring, precision agriculture, traffic management. This paper provides a comprehensive survey on the research progress and prospects of DL-based UAV object detection and tracking methods. More specifically, we first outline the challenges, statistics of existing methods, and provide solutions from the perspectives of DL-based models in three research topics: object detection from the image, object detection from the video, and object tracking from the video. Open datasets related to UAV-dominated object detection and tracking are exhausted, and four benchmark datasets are employed for performance evaluation using some state-of-the-art methods. Finally, prospects and considerations for the future work are discussed and summarized. It is expected that this survey can facilitate those researchers who come from remote sensing field with an overview of DL-based UAV object detection and tracking methods, along with some thoughts on their further developments.

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Section: A Tracking-by-detectionmentioning

confidence: 99%

Section: A Tracking-by-detectionmentioning

confidence: 99%

Section: Multiple Object Tracking Based On Memory Networkmentioning

confidence: 99%

Section: Referencementioning

confidence: 99%

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Deep Learning for UAV-based Object Detection and Tracking: A Survey

Wu,

Li,

Hong

et al. 2021

Preprint

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“…Text-to-Video Synthesis [34] Medical Imaging [55] Multi-Modal Distributionof Pedestrian Trajectories [64] Feature Filter for EEG [72] Semantic-Image-to-Photo Translation Multiple object tracking in UAV videos [35] Reconstruction of turbulent velocity fields [56] Pronunciation Fluency [65] Feature Filter for EEG [73] Generate New Human Poses…”

Section: Architecture Basedmentioning

confidence: 99%

Generative Adversarial Networks : A Survey

kakkar¹,

Singh²

2021

Preprint

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Generative Modelling has been a very extensive area of research since it finds immense use cases across multiple domains. Various models have been proposed in the recent past including Fully Visible Belief Nets, NADE, MADE, Pixel RNN Variational Auto Encoders, Markov Chain, and Generative Adversarial Networks. Amongst all the models, Generative Adversarial Networks have been consistently showing huge potential and developments in the area of Art, Music, SemiSupervised learning, Handling Missing data, Drug Discovery, and unsupervised learning. This emerging technology has reshaped the research landscape in the field of generative modeling. The research in the area of Generative Adversarial Networks (GANs) was introduced by Ian J. Goodfellow et al in 2014 [1]. However, since its inception, various models have been proposed over the years and are considered state-of-the-art models in generative modeling. In this survey, we provide a comprehensive review of the original GAN model and its modified versions, covering broad topics on model architecture, Objective and Loss Functions, and applications. First, we summarize different architectures proposed along with Objective Functions and loss functions used. Second, we cover the evolution of GAN followed by summarizing comparative analysis for various GANs. Then, we review various applications proposed by various authors which are built over these architectures in different domains. Finally, the technical challenges and several promising directions are highlighted.

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