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Visual Object Tracking
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Visual Object Tracking is an important research topic in computer vision, image understanding and pattern recognition. Given the initial state (centre location and scale) of a target in the first frame of a video sequence, the aim of Visual Object Tracking is to automatically obtain the states of the object in the subsequent video frames.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/chenshihfang/GOT
공식 코드https://github.com/chenxin-dlut/TransT
공식 코드https://github.com/chenxin-dlut/seqtrackv2
공식 코드Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking
https://github.com/cswaynecool/LSART
공식 코드https://github.com/cwhgn/EGTracker
공식 코드An equalised global graphical model-based approach for multi-camera object tracking
https://github.com/dkgupta90/re-siamnet
공식 코드https://github.com/dontfollowmeimcrazy/vot-kd-rl
공식 코드https://github.com/dukebw/SSTVOS
공식 코드SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation
https://github.com/egundogdu/CFCF
공식 코드https://github.com/event-ahu/adversarial_attack_defense
공식 코드Adversarial Attack for RGB-Event based Visual Object Tracking
https://github.com/event-ahu/coesot
공식 코드Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline
https://github.com/event-ahu/eventvot_benchmark
공식 코드Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline
https://github.com/event-ahu/mamba_fetrack
공식 코드Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking
https://github.com/event-ahu/mambaevt
공식 코드MambaEVT: Event Stream based Visual Object Tracking using State Space Model
https://github.com/event-ahu/slowfast_event_track
공식 코드Towards Low-Latency Event Stream-based Visual Object Tracking: A Slow-Fast Approach
https://github.com/facebookresearch/sam2
공식 코드https://github.com/fatemehN/TrackPGD
공식 코드TrackPGD: Efficient Adversarial Attack using Object Binary Masks against Robust Transformer Trackers
https://github.com/florinshum/ulast
공식 코드https://github.com/foolwood/DaSiamRPN
공식 코드Distractor-aware Siamese Networks for Visual Object Tracking
https://github.com/franktpmvu/NeighborTrack
공식 코드NeighborTrack: Improving Single Object Tracking by Bipartite Matching with Neighbor Tracklets
https://github.com/fredfung007/snlt
공식 코드Siamese Natural Language Tracker: Tracking by Natural Language Descriptions with Siamese Trackers
https://github.com/fregu856/ebms_regression
공식 코드https://github.com/fzh0917/STMTrack
공식 코드STMTrack: Template-free Visual Tracking with Space-time Memory Networks
https://github.com/georgedeath/initialisation-problem
공식 코드https://github.com/goutamyg/mvt
공식 코드https://github.com/goutamyg/smat
공식 코드Separable Self and Mixed Attention Transformers for Efficient Object Tracking
https://github.com/guanxiongsun/stpn
공식 코드Spatio-temporal Prompting Network for Robust Video Feature Extraction
https://github.com/guanxiongsun/vfe.pytorch
공식 코드Spatio-temporal Prompting Network for Robust Video Feature Extraction
https://github.com/gxnu-zhonglab/odtrack
공식 코드ODTrack: Online Dense Temporal Token Learning for Visual Tracking
https://github.com/hekaijie123/TATrack
공식 코드https://github.com/henglan/vasttrack
공식 코드https://github.com/hkchengrex/Cutie
공식 코드https://github.com/hon-wong/elysium
공식 코드Elysium: Exploring Object-level Perception in Videos via MLLM
https://github.com/hoqolo/sdstrack
공식 코드SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object Tracking
https://github.com/huajianup/360vot
공식 코드360VOT: A New Benchmark Dataset for Omnidirectional Visual Object Tracking
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.