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 1801–1850 of 1966 papers

TitleStatusHype
End-to-end representation learning for Correlation Filter based tracking—0
Good Features to Correlate for Visual TrackingCode0
DCFNet: Discriminant Correlation Filters Network for Visual TrackingCode0
Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking—0
A Hybrid Data Association Framework for Robust Online Multi-Object Tracking—0
Bootstrapping Labelled Dataset Construction for Cow Tracking and Behavior Analysis—0
Lucid Data Dreaming for Video Object SegmentationCode0
Planar Object Tracking in the Wild: A Benchmark—0
Simple Online and Realtime Tracking with a Deep Association MetricCode1
Need for Speed: A Benchmark for Higher Frame Rate Object TrackingCode0
Large Margin Object Tracking with Circulant Feature Maps—0
PathTrack: Fast Trajectory Annotation with Path Supervision—0
Instance Flow Based Online Multiple Object Tracking—0
Object Detection in Videos with Tubelet Proposal NetworksCode0
A Graphical Social Topology Model for Multi-Object Tracking—0
Deep Reinforcement Learning for Visual Object Tracking in Videos—0
Visual Multiple-Object Tracking for Unknown Clutter Rate—0
Robust and Real-time Deep Tracking Via Multi-Scale Domain Adaptation—0
Online multi-object tracking via robust collaborative model and sample selectionCode0
Globally Optimal Object Tracking with Fully Convolutional Networks—0
Learning Video Object Segmentation from Static ImagesCode0
Beyond standard benchmarks: Parameterizing performance evaluation in visual object tracking—0
Online Visual Multi-Object Tracking via Labeled Random Finite Set Filtering—0
Video Processing from Electro-optical Sensors for Object Detection and Tracking in Maritime Environment: A Survey—0
Convolutional Regression for Visual Tracking—0
Joint Graph Decomposition and Node Labeling: Problem, Algorithms, ApplicationsCode0
Multiple Object Tracking with Kernelized Correlation Filters in Urban Mixed TrafficCode0
Multi-Camera Occlusion and Sudden-Appearance-Change Detection Using Hidden Markovian Chains—0
POI: Multiple Object Tracking with High Performance Detection and Appearance Feature—0
Real-time Joint Tracking of a Hand Manipulating an Object from RGB-D Input—0
On Duality Of Multiple Target Tracking and Segmentation—0
Egocentric Height Estimation—0
Robust Moving Objects Detection in Lidar Data Exploiting Visual Cues—0
Similarity Mapping with Enhanced Siamese Network for Multi-Object Tracking—0
Discriminative Scale Space Tracking—0
Fast Single Shot Detection and Pose Estimation—0
Object Tracking via Dynamic Feature Selection Processes—0
Tracking System to Automate Data Collection of Microscopic Pedestrian Traffic Flow—0
Multi-Class Multi-Object Tracking using Changing Point Detection—0
Modeling and Propagating CNNs in a Tree Structure for Visual Tracking—0
Multiple objects tracking in surveillance video using color and Hu moments—0
Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual TrackingCode0
Object Detection, Tracking, and Motion Segmentation for Object-level Video Segmentation—0
A Data-driven Approach for Human Pose Tracking Based on Spatio-temporal Pictorial Structure—0
Tracking with multi-level features—0
Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object TrackingCode0
Deep Learning of Appearance Models for Online Object Tracking—0
Fully-Convolutional Siamese Networks for Object TrackingCode1
Learning feed-forward one-shot learners—0
FOMTrace: Interactive Video Segmentation By Image Graphs and Fuzzy Object Models—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