SOTAVerified

Object Tracking

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.

( Image credit: Towards-Realtime-MOT )

Papers

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Showing 1701–1750 of 1966 papers

TitleStatusHype
The Structure Transfer Machine Theory and ApplicationsCode0
TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the WildCode0
A Framework for Evaluating 6-DOF Object TrackersCode0
ClickBAIT-v2: Training an Object Detector in Real-Time—0
The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking—0
CliCR: A Dataset of Clinical Case Reports for Machine Reading ComprehensionCode0
Long-term Tracking in the Wild: A Benchmark—0
Learning Dynamic Memory Networks for Object TrackingCode0
Ocean Eddy Identification and Tracking using Neural Networks—0
Robust event-stream pattern tracking based on correlative filter—0
Patchwise object tracking via structural local sparse appearance model—0
General-Purpose Deep Point Cloud Feature ExtractorCode0
Innovative Texture Database Collecting Approach and Feature Extraction Method based on Combination of Gray Tone Difference Matrixes, Local Binary Patterns,and K-means Clustering—0
Event-based Moving Object Detection and Tracking—0
Full Reference Objective Quality Assessment for Reconstructed Background Images—0
Mono-Camera 3D Multi-Object Tracking Using Deep Learning Detections and PMBM Filtering—0
Beyond Pixels: Leveraging Geometry and Shape Cues for Online Multi-Object TrackingCode0
Depth Masked Discriminative Correlation Filter—0
A Twofold Siamese Network for Real-Time Object TrackingCode0
No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles using Cameras & LiDARsCode0
Non-rigid Object Tracking via Deep Multi-scale Spatial-temporal Discriminative Saliency Maps—0
Machine Learning Methods for Data Association in Multi-Object Tracking—0
Joint 3D Reconstruction of a Static Scene and Moving Objects—0
Tracking Noisy Targets: A Review of Recent Object Tracking Approaches—0
Saliency-Enhanced Robust Visual Tracking—0
Tracking Multiple Moving Objects Using Unscented Kalman Filtering Techniques—0
Parallel Tracking and Verifying—0
Comparative Study of ECO and CFNet Trackers in Noisy Environment—0
Improving Multiple Object Tracking with Optical Flow and Edge Preprocessing—0
Psychlab: A Psychology Laboratory for Deep Reinforcement Learning AgentsCode0
EnKCF: Ensemble of Kernelized Correlation Filters for High-Speed Object TrackingCode0
Learning Hierarchical Features for Visual Object Tracking with Recursive Neural Networks—0
Depth-Adaptive Computational Policies for Efficient Visual Tracking—0
Detect-and-Track: Efficient Pose Estimation in VideosCode0
Large-Scale Object Discovery and Detector Adaptation from Unlabeled VideoCode0
Towards dense object tracking in a 2D honeybee hiveCode0
The ParallelEye Dataset: Constructing Large-Scale Artificial Scenes for Traffic Vision Research—0
Track, then Decide: Category-Agnostic Vision-based Multi-Object Tracking—0
Multi-appearance Segmentation and Extended 0-1 Program for Dense Small Object Tracking—0
Robust Estimation of Similarity Transformation for Visual Object TrackingCode0
SOT for MOT—0
Long-Term Visual Object Tracking BenchmarkCode0
Visual Explanation by High-Level Abduction: On Answer-Set Programming Driven Reasoning about Moving Objects—0
Hierarchical Spatial-aware Siamese Network for Thermal Infrared Object TrackingCode0
MAVOT: Memory-Augmented Video Object Tracking—0
Robust Object Tracking Based on Self-adaptive Search Area—0
Discussion among Different Methods of Updating Model Filter in Object Tracking—0
Pixel-wise object tracking—0
Tracking in Aerial Hyperspectral Videos using Deep Kernelized Correlation Filters—0
Latent Constrained Correlation Filter—0
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All datasetsCOESOTFE108QuadTrackSeaDronesSeeBIRDSAI - ICVGIP 2020KITTIMMPTRACKPerception TestVisEvent

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HR-CEUTrack-LargeSuccess Rate65—Unverified
2HR-CEUTrack-BaseSuccess Rate63.2—Unverified
3CEUTrack-LargeSuccess Rate62.8—Unverified
4CEUTrack-BaseSuccess Rate62—Unverified
5SiamR-CNNSuccess Rate60.9—Unverified
6TransTSuccess Rate60.5—Unverified
7SuperDiMPSuccess Rate60.2—Unverified
8TrDiMPSuccess Rate60.1—Unverified
9KeepTrackSuccess Rate59.6—Unverified
10AiATrackSuccess Rate59—Unverified
#ModelMetricClaimedVerifiedStatus
1HR-MonTrack-BaseSuccess Rate68.5—Unverified
2HR-MonTrack-TinySuccess Rate66.3—Unverified
3Multi-modalSuccess Rate63.4—Unverified
4PrDiMPSuccess Rate59—Unverified
5DiMPSuccess Rate57.1—Unverified
6MonTrackSuccess Rate54.9—Unverified
7ATOMSuccess Rate46.5—Unverified
8KYSSuccess Rate26.6—Unverified
#ModelMetricClaimedVerifiedStatus
1OmniTrackHOTA23.45—Unverified
2DeepSORTHOTA21.16—Unverified
3OC-SORTHOTA20.83—Unverified
4ByteTrackHOTA20.66—Unverified
5TrackFormerHOTA19.62—Unverified
6HybridSORTHOTA16.64—Unverified
7DiffMOTHOTA16.4—Unverified
8Bot-SORTHOTA15.77—Unverified
#ModelMetricClaimedVerifiedStatus
1DiMP50Success Rate67.33—Unverified
2PrDiMP50Success Rate67—Unverified
3PrDiMP18Success Rate65.9—Unverified
4DiMP18Success Rate64.6—Unverified
5AtomSuccess Rate63.8—Unverified
#ModelMetricClaimedVerifiedStatus
1finalHumans0.14—Unverified
2night_furyHumans0.05—Unverified
3Yolo based methodHumans0.02—Unverified
4finalHumans0—Unverified
#ModelMetricClaimedVerifiedStatus
1M2-Trackmean precision83.4—Unverified
2BATmean precision75.2—Unverified
#ModelMetricClaimedVerifiedStatus
1UMMT3DMOTA95—Unverified
2MMPTRACK3DMOTA94.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Siam-FCAverage IOU0.66—Unverified
#ModelMetricClaimedVerifiedStatus
1RT-MDNetPrecision Plot0.63—Unverified