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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 851–875 of 1966 papers

TitleStatusHype
Know Your Surroundings: Panoramic Multi-Object Tracking by Multimodality Collaboration—0
DeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions—0
Bayesian Smoothing for the Extended Object Random Matrix Model—0
Know Your Surroundings: Exploiting Scene Information for Object Tracking—0
Keypoint-based object tracking and localization using networks of low-power embedded smart cameras—0
Bayesian Nonparametrics: An Alternative to Deep Learning—0
Bayesian nonparametric modeling for predicting dynamic dependencies in multiple object tracking—0
A Large Scale Urban Surveillance Video Dataset for Multiple-Object Tracking and Behavior Analysis—0
A comparison of extended object tracking with multi-modal sensors in indoor environment—0
Joint Spatial-Temporal Optimization for Stereo 3D Object Tracking—0
Joint Spatial-Temporal Calibration for Camera and Global Pose Sensor—0
Deformable Object Tracking with Gated Fusion—0
Joint Scene and Object Tracking for Cost-Effective Augmented Reality Assisted Patient Positioning in Radiation Therapy—0
Basketball-SORT: An Association Method for Complex Multi-object Occlusion Problems in Basketball Multi-object Tracking—0
Joint Learning of Siamese CNNs and Temporally Constrained Metrics for Tracklet Association—0
Deep Siamese Networks with Bayesian non-Parametrics for Video Object Tracking—0
BASE: Probably a Better Approach to Multi-Object Tracking—0
A Joint Intensity-Neuromorphic Event Imaging System for Resource Constrained Devices—0
Joint Inference of States, Robot Knowledge, and Human (False-)Beliefs—0
DeepScale: Online Frame Size Adaptation for Multi-object Tracking on Smart Cameras and Edge Servers—0
Joint Graph Decomposition & Node Labeling: Problem, Algorithms, Applications—0
Label and Sample: Efficient Training of Vehicle Object Detector from Sparsely Labeled Data—0
Label Space Partition Selection for Multi-Object Tracking Using Two-Layer Partitioning—0
Deep Reinforcement Learning with Iterative Shift for Visual Tracking—0
BankTweak: Adversarial Attack against Multi-Object Trackers by Manipulating Feature Banks—0
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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