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 1601–1650 of 1966 papers

TitleStatusHype
Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification—0
Background subtraction on depth videos with convolutional neural networks—0
Bayesian Smoothing for the Extended Object Random Matrix Model—0
MOANA: An Online Learned Adaptive Appearance Model for Robust Multiple Object Tracking in 3D—0
UAV-GESTURE: A Dataset for UAV Control and Gesture RecognitionCode0
Fast CNN-Based Object Tracking Using Localization Layers and Deep Features Interpolation—0
Efficient Convolutional Neural Network Training with Direct Feedback Alignment—0
Intelligent Intersection: Two-Stream Convolutional Networks for Real-time Near Accident Detection in Traffic Video—0
Pixel personality for dense object tracking in a 2D honeybee hive—0
Model-free Tracking with Deep Appearance and Motion Features Integration—0
Unified Graph based Multi-Cue Feature Fusion for Robust Visual Tracking—0
Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation—0
Fast Online Object Tracking and Segmentation: A Unifying ApproachCode2
Automatic individual pig detection and tracking in surveillance videos—0
Material Based Object Tracking in Hyperspectral Videos: Benchmark and Algorithms—0
Learning Non-Uniform Hypergraph for Multi-Object Tracking—0
Handcrafted and Deep Trackers: Recent Visual Object Tracking Approaches and Trends—0
Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics—0
The Right (Angled) Perspective: Improving the Understanding of Road Scenes Using Boosted Inverse Perspective Mapping—0
Object Tracking by Reconstruction with View-Specific Discriminative Correlation Filters—0
Eliminating Exposure Bias and Loss-Evaluation Mismatch in Multiple Object Tracking—0
Joint Monocular 3D Vehicle Detection and TrackingCode0
Multi-hierarchical Independent Correlation Filters for Visual TrackingCode0
Describe and Attend to Track: Learning Natural Language guided Structural Representation and Visual Attention for Object Tracking—0
Background Subtraction with Real-time Semantic Segmentation—0
Robust Visual Tracking using Multi-Frame Multi-Feature Joint ModelingCode0
ATOM: Accurate Tracking by Overlap MaximizationCode0
Deep Siamese Networks with Bayesian non-Parametrics for Video Object Tracking—0
Exploit the Connectivity: Multi-Object Tracking with TrackletNetCode0
Convolutional Recurrent Predictor: Implicit Representation for Multi-target Filtering and Tracking—0
GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the WildCode0
Deep Affinity Network for Multiple Object TrackingCode0
Object Tracking in Hyperspectral Videos with Convolutional Features and Kernelized Correlation Filter—0
Deep learning based 2.5D flow field estimation for maximum intensity projections of 4D optical coherence tomography—0
Visions of a generalized probability theory—0
A Robust Local Binary Similarity Pattern for Foreground Object Detection—0
Incremental Deep Learning for Robust Object Detection in Unknown Cluttered Environments—0
Automated learning with a probabilistic programming language: Birch—0
Marrying Tracking with ELM: A Metric Constraint Guided Multiple Feature Fusion Method—0
Visual Object Tracking based on Adaptive Siamese and Motion Estimation Network—0
Deformable Object Tracking with Gated Fusion—0
LaSOT: A High-quality Benchmark for Large-scale Single Object TrackingCode1
Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic DataCode0
Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-IdentificationCode0
Tracking by Animation: Unsupervised Learning of Multi-Object Attentive TrackersCode0
Rate-Adaptive Neural Networks for Spatial Multiplexers—0
DensSiam: End-to-End Densely-Siamese Network with Self-Attention Model for Object TrackingCode0
Multiple Object Tracking in Urban Traffic Scenes with a Multiclass Object Detector—0
Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing—0
Multiple Context Features in Siamese Networks for Visual Object TrackingCode0
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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