https://github.com/xiongjiechen/normalizing-flows-dpfs
공식 코드Conditional Measurement Density Estimation in Sequential Monte Carlo via Normalizing Flow
Papers with Code 아카이브
Visual Tracking
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 4
- 평가표
- 10
- 보존된 평가 기록
- 19
Visual Tracking is an essential and actively researched problem in the field of computer vision with various real-world applications such as robotic services, smart surveillance systems, autonomous driving, and human-computer interaction. It refers to the automatic estimation of the trajectory of an arbitrary target object, usually specified by a bounding box in the first frame, as it moves around in subsequent video frames.
Source: Learning Reinforced Attentional Representation for End-to-End Visual Tracking
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/xl-sr/THOR
공식 코드https://github.com/y-zheng18/point_odyssey
공식 코드PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking
https://github.com/yangchris11/samurai
공식 코드SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory
https://github.com/yangkai12/SiamCorners
공식 코드https://github.com/yoxu515/mits
공식 코드Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation
https://github.com/yuanqqq/sin
공식 코드https://github.com/zbw001/tapip3d
공식 코드https://github.com/zbzhzhy/unsupervised-spectral-reconstruction
공식 코드Semantic-embedded Unsupervised Spectral Reconstruction from Single RGB Images in the Wild
https://github.com/zhang-pengyu/HMFT
공식 코드Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline
https://github.com/zhang-pengyu/Multimodal-Tracking-Survey
공식 코드Multi-modal Visual Tracking: Review and Experimental Comparison
https://github.com/zhang-pengyu/Multimodal_tracking_survey
공식 코드Multi-modal Visual Tracking: Review and Experimental Comparison
https://github.com/zhanglichao/updatenet
공식 코드https://github.com/zhangyong-tang/gmmt
공식 코드https://github.com/zhangyong-tang/moetrack
공식 코드https://github.com/zj5559/motion-prompt-tracking
공식 코드https://github.com/zongwei97/untrack
공식 코드https://bitbucket.org/nordlinglab/nordlinglab-dfe
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/SiamFC
SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/siamRPN
SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks
https://github.com/2023-MindSpore-4/Code11/tree/main/SiamFC
SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines
https://github.com/2023-MindSpore-4/Code11/tree/main/siamRPN
SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks
https://github.com/2023-MindSpore-4/Code14/tree/main/SiamFC
SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks
https://github.com/2023-MindSpore-4/Code14/tree/main/siamRPN
SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks
https://github.com/4kubo/bacf_python
Learning Background-Aware Correlation Filters for Visual Tracking
https://github.com/BossBobxuan/RT-MDNet
https://github.com/Chinmayrane16/ReconNet-PyTorch
ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Random Measurements
https://github.com/FelixOliver/PROYECTO-FINAL-VOT
Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
https://github.com/Giveupfree/SiamCAR-CAM
SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking
https://github.com/HaHuangChan/CACFNet
DCFNet: Discriminant Correlation Filters Network for Visual Tracking
https://github.com/HengLan/LaSOT_Evaluation_Toolkit
LaSOT: A High-quality Large-scale Single Object Tracking Benchmark
https://github.com/HyeonseobNam/py-MDNet
Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
https://github.com/IlchaeJung/RT-MDNet
https://github.com/JanghoonChoi/janghoonchoi.github.io
Real-time visual tracking by deep reinforced decision making
https://github.com/JoseVillagranE/SiameseNetworks
https://github.com/KARAASLAN-AI/re3-object-tracking
Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.