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Video Object Tracking
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Video Object Detection aims to detect targets in videos using both spatial and temporal information. It's usually deeply integrated with tasks such as Object Detection and Object Tracking.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/masterbin-iiau/unicorn
공식 코드https://github.com/medicinetoken/medical-sam2
공식 코드Medical SAM 2: Segment medical images as video via Segment Anything Model 2
https://github.com/minghanli/univs
공식 코드UniVS: Unified and Universal Video Segmentation with Prompts as Queries
https://github.com/miv-xjtu/artrack
공식 코드ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe
https://github.com/msprITU/TDIOT
공식 코드TDIOT: Target-driven Inference for Deep Video Object Tracking
https://github.com/necla-ml/cater-h
공식 코드https://github.com/nju-pcalab/sttrack
공식 코드Exploiting Multimodal Spatial-temporal Patterns for Video Object Tracking
https://github.com/pblatter/ettrack
공식 코드https://github.com/researchmm/LightTrack
공식 코드LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search
https://github.com/researchmm/SiamDW
공식 코드Deeper and Wider Siamese Networks for Real-Time Visual Tracking
https://github.com/researchmm/Stark
공식 코드https://github.com/researchmm/TracKit
공식 코드https://github.com/sivandoveh/iploc
공식 코드Teaching VLMs to Localize Specific Objects from In-context Examples
https://github.com/supertyd/XTrack
공식 코드XTrack: Multimodal Training Boosts RGB-X Video Object Trackers
https://github.com/vision4robotics/ADTrack
공식 코드ADTrack: Target-Aware Dual Filter Learning for Real-Time Anti-Dark UAV Tracking
https://github.com/visionml/pytracking
공식 코드https://github.com/wenbowen123/BundleTrack
공식 코드BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D Models
https://github.com/wencheng256/SiamBAN-ACM
공식 코드Learning to Fuse Asymmetric Feature Maps in Siamese Trackers
https://github.com/wongkinyiu/yolov7
공식 코드YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/zhiqic/procontext
공식 코드ProContEXT: Exploring Progressive Context Transformer for Tracking
https://github.com/zillur-av/wrong-way-vehicle-detection
공식 코드A Real-Time Wrong-Way Vehicle Detection Based on YOLO and Centroid Tracking
https://github.com/zongwei97/untrack
공식 코드https://gitlab.com/sbonnefoy/siamese_net_change_detection
공식 코드https://github.com/2023-MindSpore-1/ms-code-218/tree/main/ocean_model
https://github.com/2023-MindSpore-1/ms-code-24
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/4kubo/bacf_python
Learning Background-Aware Correlation Filters for Visual Tracking
https://github.com/AbdurrahmanNadi/activity_recognition_web_service
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/AbhinavJhanwar/object-detection-classification
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/AlexeyAB/darknet
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/Alexyuda/action_recognition
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/Bobholamovic/CDLab
https://github.com/CMU-CREATE-Lab/deep-smoke-machine
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/CycloneBoy/PPDetectionPytorch
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/DengPingFan/Inf-Net
https://github.com/FrederikSchorr/sign-language
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.