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 1176–1200 of 1966 papers

TitleStatusHype
Opening Up Open World Tracking—0
PatchTrack: Multiple Object Tracking Using Frame Patches—0
Spiking Transformers for Event-Based Single Object Tracking—0
Multi-Object Tracking Meets Moving UAV—0
SFU-HW-Tracks-v1: Object Tracking Dataset on Raw Video Sequences—0
A Discriminative Single-Shot Segmentation Network for Visual Object Tracking—0
Adaptive Subsampling for ROI-based Visual Tracking: Algorithms and FPGA Implementation—0
Efficient Visual Tracking with Exemplar TransformersCode0
On the Dilution of Precision for Time Difference of Arrival with Station Deployment—0
Contextualized Spatio-Temporal Contrastive Learning with Self-SupervisionCode0
Few-Shot Keypoint Detection as Task Adaptation via Latent Embeddings—0
Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook—0
MMPTRACK: Large-scale Densely Annotated Multi-camera Multiple People Tracking Benchmark—0
Radar Aided Proactive Blockage Prediction in Real-World Millimeter Wave Systems—0
Implicit Equivariance in Convolutional NetworksCode0
Track Boosting and Synthetic Data Aided Drone Detection—0
Learning Dynamic Compact Memory Embedding for Deformable Visual Object Tracking—0
LMGP: Lifted Multicut Meets Geometry Projections for Multi-Camera Multi-Object Tracking—0
Neural Network Kalman filtering for 3D object tracking from linear array ultrasound data—0
Image-based monitoring of bolt loosening through deep-learning-based integrated detection and tracking—0
Unsupervised Lightweight Single Object Tracking with UHP-SOT++—0
New Performance Measures for Object Tracking under Complex Environments—0
Multiple Hypothesis Hypergraph Tracking for Posture Identification in Embryonic Caenorhabditis elegansCode0
Cross-Modal Object Tracking: Modality-Aware Representations and A Unified Benchmark—0
Survey of Deep Learning Methods for Inverse Problems—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