https://github.com/czbiohub-sf/napari-iohub
공식 코드Contrastive learning of cell state dynamics in response to perturbations
Papers with Code 아카이브
Object Tracking
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 10
- 평가표
- 10
- 보존된 평가 기록
- 43
Object tracking is the task of taking an initial set of object detections, creating a unique ID for each of the initial detections, and then tracking each of the objects as they move around frames in a video, maintaining the ID assignment. State-of-the-art methods involve fusing data from RGB and event-based cameras to produce more reliable object tracking. CNN-based models using only RGB images as input are also effective. The most popular benchmark is OTB. There are several evaluation metrics specific to object tracking, including HOTA, MOTA, IDF1, and Track-mAP.
<span style="color:grey; opacity: 0.6">( Image credit: Towards-Realtime-MOT )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/daip13/LPC_MOT
공식 코드https://github.com/daniellehot/Underwater-Fish-Environment
공식 코드https://github.com/deepmind/deepmind-research
공식 코드Attention over learned object embeddings enables complex visual reasoning
https://github.com/deepmind/perception_test
공식 코드Perception Test: A Diagnostic Benchmark for Multimodal Video Models
https://github.com/deepneuroscience/DeepThermalImaging
공식 코드https://github.com/dendorferpatrick/quovadis
공식 코드Quo Vadis: Is Trajectory Forecasting the Key Towards Long-Term Multi-Object Tracking?
https://github.com/dengpanfu/luperson-nl
공식 코드Large-Scale Pre-training for Person Re-identification with Noisy Labels
https://github.com/derryhub/fairmot-attack
공식 코드Tracklet-Switch Adversarial Attack against Pedestrian Multi-Object Tracking Trackers
https://github.com/detectRecog/PointTrack
공식 코드Segment as Points for Efficient Online Multi-Object Tracking and Segmentation
https://github.com/dinggh0817/4d_radar_mot
공식 코드https://github.com/dkgupta90/re-siamnet
공식 코드https://github.com/dlr-mi/utrack
공식 코드https://github.com/dlr-rm/3dobjecttracking
공식 코드SRT3D: A Sparse Region-Based 3D Object Tracking Approach for the Real World
https://github.com/dontfollowmeimcrazy/vot-kd-rl
공식 코드https://github.com/dstallmann/cell_cultivation_analysis
공식 코드Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation
https://github.com/dsx0511/3dmotformer
공식 코드3DMOTFormer: Graph Transformer for Online 3D Multi-Object Tracking
https://github.com/dsx0511/ada-track
공식 코드https://github.com/dukebw/SSTVOS
공식 코드SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation
https://github.com/dvl-tum/SUSHI
공식 코드Unifying Short and Long-Term Tracking with Graph Hierarchies
https://github.com/dvl-tum/ghost
공식 코드https://github.com/dvlab-research/VoxelNeXt
공식 코드VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking
https://github.com/dvlab-research/eccv22-p3aformer-tracking-objects-as-pixel-wise-distributions
공식 코드https://github.com/dvlab-research/uvtr
공식 코드Unifying Voxel-based Representation with Transformer for 3D Object Detection
https://github.com/dyhBUPT/GIAOTracker
공식 코드https://github.com/dyhBUPT/StrongSORT
공식 코드https://github.com/dyhbupt/ensemblemot
공식 코드EnsembleMOT: A Step towards Ensemble Learning of Multiple Object Tracking
https://github.com/dyhbupt/hit
공식 코드https://github.com/dyhbupt/ikun
공식 코드https://github.com/e2crawfo/silot
공식 코드Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking
https://github.com/ecker-lab/object-centric-representation-benchmark
공식 코드Benchmarking Unsupervised Object Representations for Video Sequences
https://github.com/eddyhkchiu/DMSTrack
공식 코드https://github.com/eddyhkchiu/mahalanobis_3d_multi_object_tracking
공식 코드Probabilistic 3D Multi-Object Tracking for Autonomous Driving
https://github.com/efs-opensource/calibration-framework
공식 코드Parametric and Multivariate Uncertainty Calibration for Regression and Object Detection
https://github.com/egundogdu/CFCF
공식 코드https://github.com/eleboss/ebr
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.