{"id":13440615,"url":"https://github.com/SherryJYC/paper-MTMC","last_synced_at":"2025-03-20T10:31:38.901Z","repository":{"id":44490672,"uuid":"326004229","full_name":"SherryJYC/paper-MTMC","owner":"SherryJYC","description":"A repo of awesome papers about multi target multi camera tracking","archived":false,"fork":false,"pushed_at":"2022-11-30T10:37:15.000Z","size":66,"stargazers_count":178,"open_issues_count":0,"forks_count":20,"subscribers_count":9,"default_branch":"main","last_synced_at":"2024-07-02T00:31:25.143Z","etag":null,"topics":["deep-learning","multicamera","tracking"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/SherryJYC.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2021-01-01T15:25:24.000Z","updated_at":"2024-05-28T05:36:39.000Z","dependencies_parsed_at":"2022-08-12T11:20:20.977Z","dependency_job_id":null,"html_url":"https://github.com/SherryJYC/paper-MTMC","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SherryJYC%2Fpaper-MTMC","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SherryJYC%2Fpaper-MTMC/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SherryJYC%2Fpaper-MTMC/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SherryJYC%2Fpaper-MTMC/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SherryJYC","download_url":"https://codeload.github.com/SherryJYC/paper-MTMC/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":213299285,"owners_count":15566595,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","multicamera","tracking"],"created_at":"2024-07-31T03:01:24.443Z","updated_at":"2024-10-28T00:30:22.827Z","avatar_url":"https://github.com/SherryJYC.png","language":null,"funding_links":[],"categories":["Others"],"sub_categories":[],"readme":"# MTMC\nA paper list of Multi Target Multi Camera (MTMC) tracking and related topics \u003cbr/\u003e\nincluding application case in: vehicle tracking :red_car: , pedestrian tracking :frowning_person: , sports player tracking :soccer: . \n\n\u003cdetails\u003e\u003csummary\u003eClick to show menu\u003c/summary\u003e\n\u003cp\u003e\n\n1. \u003ca href=\"#multi-target-single-camera-tracking-paper\"\u003eMulti Target Single Camera Tracking Paper \u003c/a\u003e \u003cbr/\u003e\n2. \u003ca href=\"#multi-target-multi-camera-tracking-paper\"\u003eMulti Target Multi Camera Tracking Paper \u003c/a\u003e \u003cbr/\u003e\n3. \u003ca href=\"#related-github-repo\"\u003eRelated Github Repo\u003c/a\u003e \u003cbr/\u003e\n4. \u003ca href=\"#related-competition\"\u003eRelated Competition\u003c/a\u003e \u003cbr/\u003e\n\u003c!--\n5. \u003ca href=\"#related-group-or-researcher\"\u003eRelated Group or Researcher\u003c/a\u003e\n--\u003e\n\u003c/p\u003e\n\u003c/details\u003e\n\n## Multi Target Single Camera Tracking Paper \n\n### 2022\n- Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking, Cao et al. [[paper]](https://arxiv.org/abs/2203.14360) [[code]](https://github.com/noahcao/OC_SORT)\n\u003e interesting to see a variant of SORT (observation-centered) achieve decent results \n\n- PoserNet: Refining Relative Camera Poses Exploiting Object Detections, Taiana et al. :rainbow: [[paper]](https://arxiv.org/pdf/2207.09445.pdf) [[code]](https://github.com/IIT-PAVIS/PoserNet)\n\u003e not tracking but seems applicable in MC-tracking, detect bbox from images and match roughly, use interesting GNN formulation to refine camera pose: image as node, edge as relative pose, bbox info added during message passing\n\n### 2021\n- ByteTrack: Multi-Object Tracking by Associating Every Detection Box, Zhang et al. [[paper]](https://arxiv.org/abs/2110.06864) [[code]](https://github.com/ifzhang/ByteTrack)\n\u003e at first associate box with high detection score, then associate box with low detection score, improve tracking on occluded objects\n\n- Quasi-Dense Similarity Learning for Multiple Object Tracking, Pang et al. :rainbow: [[paper]](https://arxiv.org/abs/2006.06664) [[code]](https://github.com/SysCV/qdtrack)\n\u003e instance similarity learning based on region proposal, flexible, no external data required\n\n- TrackFormer: Multi-Object Tracking with Transformers, Meinhardt et al. [[paper]](https://arxiv.org/abs/2101.02702)\n\u003e Transformer, detection and tracking simultaneously\n\n### 2020\n- How To Train Your Deep Multi-Object Tracker, Xu et al. :rainbow: [[paper]](https://arxiv.org/abs/1906.06618)\n\u003e Deep Hungarian Net, approximate MOTA, MOTP for loss function directly\n\n- Learning a Neural Solver for Multiple Object Tracking, Braso \u0026 Leal-Taixe :rainbow: [[paper]](https://arxiv.org/abs/1912.07515)\n\u003e apperance embedding (node) and geometry distance embedding (edge) for graph, edge classification with cross entropy loss \n\n- Deep learning in video multi-object tracking: A survey, Ciaparrone et al. [[paper]](https://arxiv.org/abs/1907.12740)\n\u003e pipeline: detection, feature extraction, affinity, association\n\n- Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking, Peng et al. :rainbow:  [[paper]](https://arxiv.org/abs/2007.14557) [[code]](https://github.com/pjl1995/CTracker) \n\u003e end-to-end MOT, use adjacent frames (chained) to combine detection, feature extraction and tracking \n\n### 2019\n- Spatial-Temporal Relation Networks for Multi-Object Tracking, Xu et al. [[paper]](https://openaccess.thecvf.com/content_ICCV_2019/papers/Xu_Spatial-Temporal_Relation_Networks_for_Multi-Object_Tracking_ICCV_2019_paper.pdf)\n\u003e use appearance, location and topology cues for similarity score, then graph solved by Hungarian algorithm\n\n- Graph convolutional tracking, Gao et al. [[paper]](https://openaccess.thecvf.com/content_CVPR_2019/papers/Gao_Graph_Convolutional_Tracking_CVPR_2019_paper.pdf)\n\u003e GNN, Siamese network\n\n- Tracking without bells and whistles, Bergmann et al. [[paper]](https://arxiv.org/abs/1903.05625) [[code]](https://github.com/phil-bergmann/tracking_wo_bnw)\n\u003e motion and appearance extention -\u003e Tracktor++\n\n- Deep Learning for Visual Tracking: A Comprehensive Survey, Marvasti-Zadeh et al. [[paper]](https://arxiv.org/abs/1912.00535)\n\u003e traditional and deep visual trackers \n\n- A Review of Visual Trackers and Analysis of its Application to Mobile Robot, You et al. [[paper]](https://arxiv.org/abs/1910.09761)\n\u003e correlation filter, deep learning and convolutional features\n\n### 2018\n\n- Exploit the Connectivity: Multi-Object Tracking with TrackletNet, Wang et al. [[paper]](https://arxiv.org/abs/1811.07258)\n\u003e use epipolar geometry, tracklet as node in graph\n\n- Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification, Chen et al. [[paper]](https://arxiv.org/abs/1809.04427)[[code]](https://github.com/longcw/MOTDT)\n\u003e online MOT tracker\n\n### 2017\n- Multi-Object Tracking with Quadruplet Convolutional Neural Networks, Son et al. [[paper]](https://openaccess.thecvf.com/content_cvpr_2017/papers/Son_Multi-Object_Tracking_With_CVPR_2017_paper.pdf)\n\u003e learn statistics to normalize effect of camera poses, temporal adjacent constraint for data association \n\n- Real-Time Multiple Object Tracking, Murray. [[paper]](https://www.diva-portal.org/smash/get/diva2:1146388/FULLTEXT01.pdf)\n\u003e not use appearance feature, very fast, not accurate\n\n- High-Speed Tracking-by-Detection Without Using Image Information, Bochinski et al. [[paper]](http://elvera.nue.tu-berlin.de/files/1517Bochinski2017.pdf) [[code]](https://github.com/bochinski/iou-tracker)\n\u003e IoU tracker, no visual cues used, fast  \n\n- Online Multi-Target Tracking Using Recurrent Neural Networks, Milan et al. [[paper]](https://arxiv.org/abs/1604.03635)\n\u003e RNN as tracker, LSTM for data association\n\n### 2016\n- Learning by tracking: Siamese CNN for robust target association, Leal-Taixe et al. [[paper]](https://arxiv.org/abs/1604.07866)\n\u003e use Siamese CNN to learn similarity, for data association, graph solved by Linear Programming \n\n### 2014\n- Learning an image-based motion context for multiple people tracking, Leal-Taixe et al. [[paper]](https://ieeexplore.ieee.org/document/6909848)\n\u003e interaction between objects, relax the dependency of tracking on detections\n\n\n## Multi Target Multi Camera Tracking Paper\n\n### 2022\n- Graph Convolutional Network for Multi-Target Multi-Camera Vehicle Tracking, Luna et al. [[paper]](https://arxiv.org/pdf/2211.15538.pdf)\n\u003e step 1: single camera tracking \u0026 generate appearance feature, step 2: multi camera association with GNN (single camera trajectories as node, averaged feature as node feature, cos(feature) as edge feature), weighted loss for imbalance\n\n### 2021\n- DyGLIP: A Dynamic Graph Model with Link Prediction for Accurate Multi-Camera Multiple Object Tracking, Quach et al. [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Quach_DyGLIP_A_Dynamic_Graph_Model_With_Link_Prediction_for_Accurate_CVPR_2021_paper.pdf)\n\u003e tracklet as node, link prediction for data association, ok for w/wo overalaping view, use large training data \n\n- Online Clustering-based Multi-Camera Vehicle Tracking in Scenarios with overlapping FOVs, Luna et al. [[paper]](https://arxiv.org/pdf/2102.04091.pdf)\n\u003e detection-\u003e feature extraction, homography -\u003e cross-camera cluster -\u003e incremental temporal association, small latency, not very accurate\n\n### 2020\n\n- Real-time 3D Deep Multi-Camera Tracking, You \u0026 Jiang [[paper]](https://arxiv.org/abs/2003.11753)\n\u003e fusion all views into ground-plane occupancy heatmap \n\n- City-Scale Multi-Camera Vehicle Tracking by Semantic Attribute Parsing and Cross-Camera Tracklet Matching, He et al. [[paper]](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w35/He_City-Scale_Multi-Camera_Vehicle_Tracking_by_Semantic_Attribute_Parsing_and_Cross-Camera_CVPRW_2020_paper.pdf)\n\u003e tracklet representation with spatial-temporal attention, then tracklet-to-target assignment\n\n- Multi-Target Multi-Camera Tracking by Tracklet-to-Target Assignment, He et al. [[paper]](https://ieeexplore.ieee.org/document/9042858) [[code]](https://github.com/GehenHe/TRACTA)\n\u003e tracklet-to-target assignment\n\n- AI City Challenge 2020 – Computer Vision for Smart Transportation Applications, Chang et al. [[paper]](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w35/Chang_AI_City_Challenge_2020_-_Computer_Vision_for_Smart_Transportation_CVPRW_2020_paper.pdf)\n\u003e single camera tracklet -\u003e multi-camera tracklet fusion with appearance and physical features\n\n- Multi-Camera Tracking of Vehicles based on Deep Features Re-ID and Trajectory-Based Camera Link Models, Hsu et al. [[paper]](https://openaccess.thecvf.com/content_CVPRW_2019/papers/AI%20City/Hsu_Multi-Camera_Tracking_of_Vehicles_based_on_Deep_Features_Re-ID_and_CVPRW_2019_paper.pdf)\n\u003e use TrackletNet for single camera trajectory -\u003e inter-camera tracking\n\n- ELECTRICITY: An Efficient Multi-camera Vehicle Tracking System for Intelligent City, Qian et al. [[paper]](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w35/Qian_ELECTRICITY_An_Efficient_Multi-Camera_Vehicle_Tracking_System_for_Intelligent_City_CVPRW_2020_paper.pdf)\n\u003e single camera tracking -\u003e match tracklets across camera views\n\n- Pose-Assisted Multi-Camera Collaboration for Active Object Tracking, Li et al. [[paper]](https://arxiv.org/abs/2001.05161) [[code]](https://github.com/LilJing/pose-assisted-collaboration)\n\u003e Reinforcement learning, collaborative multi-camera\n\n- Reconstruction of 3D flight trajectories from ad-hoc camera networks, Li et al. [[paper]](https://arxiv.org/abs/2003.04784) [[code]](https://github.com/CenekAlbl/mvus)\n\u003e camera synchronization, SfM, Bundle Adjustment, spline representation for drone trajectory  \n\n- The MTA Dataset for Multi Target Multi Camera Pedestrian Tracking\nby Weighted Distance Aggregation [[paper]](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w70/Kohl_The_MTA_Dataset_for_Multi-Target_Multi-Camera_Pedestrian_Tracking_by_Weighted_CVPRW_2020_paper.pdf)\n\u003e combine appearance and homography for hierachical clustering, known camera pose\n\n- Cross-View Tracking for Multi-Human 3D Pose Estimation at over 100 FPS, Chen et al. [[paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_Cross-View_Tracking_for_Multi-Human_3D_Pose_Estimation_at_Over_100_CVPR_2020_paper.pdf) \n\n### 2019\n- People tracking in multi-camera systems: a review, Iguernaissi et al. [[paper]](https://link.springer.com/article/10.1007/s11042-018-6638-5)\n\u003e Centralized (combine cross-camera views before tracking, like Wen et al.) and Distributed methods (single-camera tracking before fusion)\n\n- CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification, Tang et al. [[paper]](https://arxiv.org/abs/1903.09254)\n\n- Real-Time Multi-Target Multi-Camera Tracking with Spatial-Temporal Information, Zhang \u0026 Izquierdo :rainbow: [[paper]](https://ieeexplore.ieee.org/document/8965845)\n\u003e single camera detection -\u003e create/match to track, with apperance, motion, spatial-temporal cues (cross-camera)\n\n### 2018\n- Features for Multi-Target Multi-Camera Tracking and Re-Identification, Ristani \u0026 Tomasi [[paper]](https://arxiv.org/abs/1803.10859) [[code]](https://github.com/SamvitJ/Duke-DeepCC)\n\u003e tracklet -\u003e single camera trajectory (correlation clustering) -\u003e multi camera trajectory\n\n- Vehicle Re-Identification with the Space-Time Prior, Wu et al. [[paper]](https://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w3/Wu_Vehicle_Re-Identification_With_CVPR_2018_paper.pdf) [[code]](https://github.com/cw1204772/AIC2018_iamai)\n\u003e single camera tracking -\u003e CNN feature extraction -\u003e multi camera tracking (KMeans)\n\n### 2017\n- Multi-Camera Multi-Target Tracking with Space-Time-View Hyper-graph, Wen et al. :rainbow: [[paper]](https://link.springer.com/article/10.1007/s11263-016-0943-0)\n\u003e 3D position for affinity computation, need know camera parameters, cross-view coupling before trajectory\n\n### 2014\n- Persistent Tracking for Wide Area Aerial Surveillance, Prokaj \u0026 Medioni :rainbow: [[paper]](https://ieeexplore.ieee.org/document/6909551)\n\u003e two tracker (detection and regression) in parallel, measure their correspondence\n\n### 2013\n- Hypergraphs for joint multi-view reconstruction and multi-object tracking, Hofmann et al. :rainbow: [[paper]](https://ieeexplore.ieee.org/document/6619312) [[code]](https://github.com/neohanju/HYPERGRAPH_TRACKING)\n\u003e detection as node in hypergraph to find 3d reconstruction, which is node in a min-cost flow graph, solved by binary linear programming\n\n### 2012\n- Branch-and-price global optimization for multi-view multi-target tracking, Leal-Taixé et al. [[paper]](https://www.researchgate.net/publication/261200087_Branch-and-price_global_optimization_for_multi-view_multi-target_tracking)\n\n## Related Github Repo\n- [Multi-camera live object tracking](https://github.com/LeonLok/Multi-Camera-Live-Object-Tracking)\n\n- [Resource collection about multi camera network](https://github.com/YanLu-nyu/Awesome-Multi-Camera-Network)\u003cbr/\u003e\n\n- [Recource collection about multi object tracking](https://github.com/nightmaredimple/Multi-object-Tracking-paper-code-list)\n\n- [Multi Object Tracking Paper List](https://github.com/SpyderXu/multi-object-tracking-paper-list)\n\n- [UAV detection and tracking](https://github.com/tau-adl/Detection_Tracking_JetsonTX2)\u003cbr/\u003e\n\n- [Resource collection about person reid dataset](https://github.com/NEU-Gou/awesome-reid-dataset)\u003cbr/\u003e\n\n- [OpenMMLab: toolbox for SOT, MOT](https://github.com/open-mmlab/mmtracking)\n\n- [DeepOcculusion](https://github.com/pierrebaque/DeepOcclusion)\n\n- [MOT Metrics library (Python)](https://github.com/cheind/py-motmetrics)\n\n- [MOT Metrics library (Python) 2](https://github.com/Videmo/pymot)\n\n- [Multi camera person tracker for synthetic data](https://github.com/koehlp/wda_tracker)\n\n## Related Dataset\n- [Multi Track Auto (GTA)](https://github.com/schuar-iosb/mta-dataset) [[baseline provided](https://github.com/koehlp/wda_tracker)]\n\n- [BDD100K large driving dataset](https://github.com/bdd100k/bdd100k)\n\n- [Visual Tracker Benchmark](http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html)\n\n- [DJI Drone Images](https://github.com/chuanenlin/drone-net)\n\n## Related Competition\n- [AI City Challenge](https://www.aicitychallenge.org/)\n\n- [Anti-UAV Challenge](https://anti-uav.github.io/)\n\n- [Waymo Open Dataset Challenge](https://waymo.com/open/challenges)\n\n- [SoccerNet](https://www.soccer-net.org/home)\n\n\u003c!--\n## Related Group or Researcher\n\n- [Dynamic Vision and Learning Group, TUM](https://dvl.in.tum.de/research/)\n\u003e TrackFormer, Tracktor++, Siamese\n\n- [CVLab, EPFL](https://www.epfl.ch/labs/cvlab/research/research-surv/research-body-surv-index-php/)\n\u003e Probabilistic Occupancy Map\n\n\n\u003c!--\n[DeepSORT](https://github.com/nwojke/deep_sort) \u003cbr/\u003e\n\u003cbr/\u003e\n\u003cbr/\u003e\n--\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FSherryJYC%2Fpaper-MTMC","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FSherryJYC%2Fpaper-MTMC","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FSherryJYC%2Fpaper-MTMC/lists"}