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 1351–1375 of 1966 papers

TitleStatusHype
In Defense of Color-Based Model-Free Tracking—0
Inference for multiple object tracking: A Bayesian nonparametric approach—0
Self-Assessment for Single-Object Tracking in Clutter Using Subjective Logic—0
Self-Paced Learning for Long-Term Tracking—0
Self-Supervised Cross-View Correspondence with Predictive Cycle Consistency—0
Self-Supervised Multi-Object Tracking For Autonomous Driving From Consistency Across Timescales—0
Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving—0
Self-Supervised Representation Learning from Temporal Ordering of Automated Driving Sequences—0
Semi-Supervised Object Detection with Sparsely Annotated Dataset—0
Semi-TCL: Semi-Supervised Track Contrastive Representation Learning—0
Sensitivity of Room Impulse Responses in Changing Acoustic Environment—0
SFTrack: A Robust Scale and Motion Adaptive Algorithm for Tracking Small and Fast Moving Objects—0
SFU-HW-Tracks-v1: Object Tracking Dataset on Raw Video Sequences—0
Shape and Color Object Tracking for Real-Time Robotic Navigation—0
Shape-Tailored Local Descriptors and Their Application to Segmentation and Tracking—0
Shape Tracking With Occlusions via Coarse-To-Fine Region-Based Sobolev Descent—0
SharkTrack: an accurate, generalisable software for streamlining shark and ray underwater video analysis—0
ShaSTA-Fuse: Camera-LiDAR Sensor Fusion to Model Shape and Spatio-Temporal Affinities for 3D Multi-Object Tracking—0
ShaSTA: Modeling Shape and Spatio-Temporal Affinities for 3D Multi-Object Tracking—0
SiamRCR: Reciprocal Classification and Regression for Visual Object Tracking—0
SiamReID: Confuser Aware Siamese Tracker with Re-identification Feature—0
SiamSNN: Siamese Spiking Neural Networks for Energy-Efficient Object Tracking—0
SiamTHN: Siamese Target Highlight Network for Visual Tracking—0
siftservice.com - Turning a Computer Vision algorithm into a World Wide Web Service—0
Significant changes in EEG neural oscillations during different phases of three-dimensional multiple object tracking task (3D-MOT) imply different roles for attention and working memory—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