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Multi-Object Tracking

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하위 태스크: 6
평가표: 22
보존된 평가 기록: 227

Multi-Object Tracking is a task in computer vision that involves detecting and tracking multiple objects within a video sequence. The goal is to identify and locate objects of interest in each frame and then associate them across frames to keep track of their movements over time. This task is challenging due to factors such as occlusion, motion blur, and changes in object appearance, and is typically solved using algorithms that integrate object detection and data association techniques.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/gerardmaggiolino/deep-oc-sort

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Deep OC-SORT: Multi-Pedestrian Tracking by Adaptive Re-Identification

https://github.com/giantai/pixtrack

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PixTrack: Precise 6DoF Object Pose Tracking using NeRF Templates and Feature-metric Alignment

https://github.com/git-disl/emo

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Fast and Resource-Efficient Object Tracking on Edge Devices: A Measurement Study

https://github.com/gitmehrdad/sfsort

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SFSORT: Scene Features-based Simple Online Real-Time Tracker

https://github.com/hailanyi/virconv

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Virtual Sparse Convolution for Multimodal 3D Object Detection

https://github.com/hakanardo/ggdtrack

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Multi Target Tracking by Learning from Generalized Graph Differences

https://github.com/hejiawei2023/ug3dmot

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3D Multi-Object Tracking Based on Uncertainty-Guided Data Association

https://github.com/helicopt/framot

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Towards Frame Rate Agnostic Multi-Object Tracking

https://github.com/heruihr/rallytrack

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D$^{\bf{3}}$: Duplicate Detection Decontaminator for Multi-Athlete Tracking in Sports Videos

https://github.com/holmescao/TOPICTrack

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TOPIC: A Parallel Association Paradigm for Multi-Object Tracking under Complex Motions and Diverse Scenes

https://github.com/hou-yz/DeepCC-local

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Locality Aware Appearance Metric for Multi-Target Multi-Camera Tracking

https://github.com/hsiangwei0903/Deep-EIoU

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Iterative Scale-Up ExpansionIoU and Deep Features Association for Multi-Object Tracking in Sports

https://github.com/hsiangwei0903/SportsMOT

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Observation Centric and Central Distance Recovery on Sports Player Tracking

https://github.com/huckiyang/MOT-Kuzushiji-Fashion-Video

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Robust Unsupervised Multi-Object Tracking in Noisy Environments

https://github.com/hustvl/sparsetrack

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SparseTrack: Multi-Object Tracking by Performing Scene Decomposition based on Pseudo-Depth

https://github.com/hwcao17/pkf

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PKF: Probabilistic Data Association Kalman Filter for Multi-Object Tracking

https://github.com/hyunjs/sgt

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Detection Recovery in Online Multi-Object Tracking with Sparse Graph Tracker

https://github.com/iai-hrc/ha-vid

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HA-ViD: A Human Assembly Video Dataset for Comprehensive Assembly Knowledge Understanding

https://github.com/ifzhang/ByteTrack

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ByteTrack: Multi-Object Tracking by Associating Every Detection Box

https://github.com/ifzhang/FairMOT

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FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/immortaltracker/immortaltracker

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Immortal Tracker: Tracklet Never Dies

https://github.com/inakierregueab/yolo11-jde

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YOLO11-JDE: Fast and Accurate Multi-Object Tracking with Self-Supervised Re-ID

https://github.com/jackwoo0831/depthmot

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DepthMOT: Depth Cues Lead to a Strong Multi-Object Tracker

https://github.com/james128333/HarDNet-MSEG

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HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS

https://github.com/jeanollion/distnet

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DistNet: Deep Tracking by displacement regression: application to bacteria growing in the Mother Machine

https://github.com/jhl-hust/trasw

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Tracklet-Switch Adversarial Attack against Pedestrian Multi-Object Tracking Trackers

https://github.com/jiaweihe1996/GMTracker

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Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Tracking

https://github.com/joe-rabbit/videoqa_pilot_study

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Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks

https://github.com/julianolagana/mt3v2

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Can Deep Learning be Applied to Model-Based Multi-Object Tracking?

https://github.com/jumabek/lite

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LITE: A Paradigm Shift in Multi-Object Tracking with Efficient ReID Feature Integration

https://github.com/kemo-huang/bitrack

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BiTrack: Bidirectional Offline 3D Multi-Object Tracking Using Camera-LiDAR Data

https://github.com/kemo-huang/jmodt

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Joint Multi-Object Detection and Tracking with Camera-LiDAR Fusion for Autonomous Driving

https://github.com/kitware/mevid

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MEVID: Multi-view Extended Videos with Identities for Video Person Re-Identification

https://github.com/kuanchihhuang/moma-m3t

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Delving into Motion-Aware Matching for Monocular 3D Object Tracking

https://github.com/lab206/echotrack

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EchoTrack: Auditory Referring Multi-Object Tracking for Autonomous Driving

https://github.com/leandro-svg/hybridtrack

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HybridTrack: A Hybrid Approach for Robust Multi-Object Tracking

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