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 1301–1325 of 1966 papers

TitleStatusHype
Multi-Object Tracking using Poisson Multi-Bernoulli Mixture Filtering for Autonomous Vehicles—0
Deep 6-DoF Tracking of Unknown Objects for Reactive Grasping—0
Learning Irreducible Representations of Noncommutative Lie Groups—0
Optimized Object Tracking Technique Using Kalman Filter—0
Efficient data-driven encoding of scene motion using Eccentricity—0
Multiple Convolutional Features in Siamese Networks for Object TrackingCode0
The SpaceNet Multi-Temporal Urban Development Challenge—0
Phase Space Reconstruction Network for Lane Intrusion Action Recognition—0
The Multi-Temporal Urban Development SpaceNet DatasetCode0
MOTS R-CNN: Cosine-margin-triplet loss for multi-object tracking—0
Revisiting the details when evaluating a visual trackerCode0
COLLIDE-PRED: Prediction of On-Road Collision From Surveillance Videos—0
A two-stage data association approach for 3D Multi-object Tracking—0
Semi-Automatic Annotation For Visual Object TrackingCode0
TrackMPNN: A Message Passing Graph Neural Architecture for Multi-Object Tracking—0
Multi-object Tracking with a Hierarchical Single-branch Network—0
TGCN: Time Domain Graph Convolutional Network for Multiple Objects TrackingCode0
Weakly Supervised Multi-Object Tracking and Segmentation—0
EMPIRICAL UPPER BOUND IN OBJECT DETECTION—0
A General Recurrent Tracking Framework Without Real Data—0
Beyond the Pixels: Exploring the Effects of Bit-Level Network and File Corruptions on Video Model Robustness—0
Temporally-Transferable Perturbations: Efficient, One-Shot Adversarial Attacks for Online Visual Object Trackers—0
GAKP: GRU Association and Kalman Prediction for Multiple Object Tracking—0
Coarse-to-Fine Object Tracking Using Deep Features and Correlation FiltersCode0
Limitation of Acyclic Oriented Graphs Matching as Cell Tracking Accuracy Measure when Evaluating Mitosis—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