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/wangrui1996/caffe-ssd
https://github.com/wangxiao5791509/DeepMTA_PyTorch
https://github.com/wangxiao5791509/SiamDW_tracker_revised
Deeper and Wider Siamese Networks for Real-Time Visual Tracking
https://github.com/wangxiao5791509/Tracking-with-Deep-Reinforcement-Learning
https://github.com/watarus0208/ssd_keras
https://github.com/wayne1204/NOAA-fish-finding
https://github.com/weidi1024/caffe-ssd-correct
https://github.com/wenbowen123/BundleTrack
You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration
https://github.com/wj1tr0y/SSD
https://github.com/wk738126046/deploy
https://github.com/xTRam1/Object-Detection-on-Custom-Dataset
https://github.com/xiaohaoChen/rrc_detection
https://github.com/xingpingdong/CLNet-tracking
SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks
https://github.com/xiuyu0000/new_papers_codes/tree/main/OSVOS
https://github.com/xiuyu0000/papers_with_examples/tree/main/case_10_ssd300_mobilenetv2_voc2012_detection
https://github.com/xiuyu0000/vision/blob/main/mindvision/msdetection/models/ssd.py
https://github.com/xuduo35/STAPLE
https://github.com/xuefeng-zhu5/cdaat
https://github.com/xyfer17/ssd-caffe
https://github.com/xzgz/caffe-ssd
https://github.com/yangyucheng000/Paper-4/tree/main/ProC-KD-main
ProContEXT: Exploring Progressive Context Transformer for Tracking
https://github.com/yangyucheng000/University/tree/main/model-3/sam
Underwater Camouflaged Object Tracking Meets Vision-Language SAM2
https://github.com/yangyucheng000/papercode-2/tree/main/ProC-KD-main
ProContEXT: Exploring Progressive Context Transformer for Tracking
https://github.com/yczhang1017/SSD_resnet_pytorch
https://github.com/yeqiuyi/caffe-ssd
https://github.com/yoxu515/aot-benchmark
Associating Objects with Transformers for Video Object Segmentation
https://github.com/yqyao/ssd.pytorch
https://github.com/yuhsijen/Object-Detector
https://github.com/yuhsuanlin23130/Vehicle-Detection-2019
https://github.com/yuqj1990/caffe_deeplearning_train
https://github.com/yuqj1990/caffe_train
https://github.com/yuqj1990/deepano_face_train
https://github.com/yuqj1990/deepano_train
https://github.com/yuqj1990/face_train
https://github.com/zhangxiutao/ROLO
Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.