Papers with Code 아카이브
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/chandra411/Product-Detection
https://github.com/chaowang1994/caffe-ssd
https://github.com/chenjian123456/caffe-ssd
https://github.com/chenjunweii/mxnet-ssd
https://github.com/chihyanghsu0805/object_detection_yolo
https://github.com/code-implementation1/Code6/tree/main/OSVOS
https://github.com/code-implementation1/Code8/tree/main/SiamFC
SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines
https://github.com/code-implementation1/Code9/tree/main/SSD
https://github.com/code-implementation1/Code9/tree/main/ocean_model
https://github.com/cyx2016/siamrpn_mindspore
https://github.com/dcnieho/segment-anything-2
https://github.com/debjani-bhowmick/harmonic-net-pytorch
https://github.com/dscv-lab/KMF2JMTtracking
Robust Visual Tracking using Multi-Frame Multi-Feature Joint Modeling
https://github.com/dugusiqing/caffe
https://github.com/eddiebarry/SingleShotDetector
https://github.com/ethanhou99/ml_learn
https://github.com/ezelikman/anonymal
Fast Online Object Tracking and Segmentation: A Unifying Approach
https://github.com/facebookresearch/segment-anything
https://github.com/fanneng/caffe
https://github.com/feizc/Object-Detection-Pytorch
https://github.com/fengyang95/pyCFTrackers
https://github.com/foolwood/RASNet
https://github.com/foolwood/SiamMask
Fast Online Object Tracking and Segmentation: A Unifying Approach
https://github.com/freeniliang/caffe-ssd
https://github.com/gmt710/caffe-ssd
https://github.com/gmt710/caffe_gpu
https://github.com/guanfuchen/video_obj
Fast and Accurate Online Video Object Segmentation via Tracking Parts
https://github.com/guptarohit994/ECE285_Graduate_Descent_SSD
https://github.com/gurkirt/AMTNet
https://github.com/gurkirt/realtime-action-detection
https://github.com/hidekazu300/test
https://github.com/huytranvan2010/SSD
https://github.com/hyh21521038/ssd-plate_detection
https://github.com/ibaiGorordo/ONNX-SAM2-Segment-Anything
https://github.com/idea-research/grounded-sam-2
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.