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 851–900 of 1966 papers

TitleStatusHype
RGBD1K: A Large-scale Dataset and Benchmark for RGB-D Object TrackingCode1
Learning Spatial-Frequency Transformer for Visual Object TrackingCode1
InterTrack: Interaction Transformer for 3D Multi-Object Tracking—0
Automatic Controlling Fish Feeding Machine using Feature Extraction of Nutriment and Ripple Behavior—0
AVisT: A Benchmark for Visual Object Tracking in Adverse Visibility—0
Real-Time Accident Detection in Traffic Surveillance Using Deep LearningCode1
Towards Sequence-Level Training for Visual TrackingCode1
Exploring Point-BEV Fusion for 3D Point Cloud Object Tracking with TransformerCode1
LMOT: Efficient Light-Weight Detection and Tracking in CrowdsCode1
Visual-Inertial Multi-Instance Dynamic SLAM with Object-level RelocalisationCode1
Fast Online and Relational TrackingCode1
Robust Multi-Object Tracking by Marginal Inference—0
Transformer-based assignment decision network for multiple object trackingCode1
SOMPT22: A Surveillance Oriented Multi-Pedestrian Tracking Dataset—0
PolarMOT: How Far Can Geometric Relations Take Us in 3D Multi-Object Tracking?—0
A Multi-body Tracking Framework -- From Rigid Objects to Kinematic StructuresCode0
Local Perception-Aware Transformer for Aerial TrackingCode0
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
Why Accuracy Is Not Enough: The Need for Consistency in Object Detection—0
TransFiner: A Full-Scale Refinement Approach for Multiple Object Tracking—0
Large-displacement 3D Object Tracking with Hybrid Non-local OptimizationCode1
Tracking Every Thing in the WildCode1
3D Siamese Transformer Network for Single Object Tracking on Point CloudsCode1
Video object tracking based on YOLOv7 and DeepSORT—0
Split Happens! Imprecise and Negative Information in Gaussian Mixture Random Finite Set FilteringCode0
Trajectory PMB Filters for Extended Object Tracking Using Belief PropagationCode1
AiATrack: Attention in Attention for Transformer Visual TrackingCode1
MOTCOM: The Multi-Object Tracking Dataset Complexity Metric—0
The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and CountingCode1
A Certifiable Security Patch for Object Tracking in Self-Driving Systems via Historical Deviation Modeling—0
DIMBA: Discretely Masked Black-Box Attack in Single Object Tracking—0
GUSOT: Green and Unsupervised Single Object Tracking for Long Video Sequences—0
Multi-Object Tracking and Segmentation via Neural Message PassingCode1
Towards Grand Unification of Object TrackingCode2
SpOT: Spatiotemporal Modeling for 3D Object Tracking—0
Tracking Objects as Pixel-wise DistributionsCode1
SRRT: Exploring Search Region Regulation for Visual Object Tracking—0
Depth Perspective-aware Multiple Object Tracking—0
TGRMPT: A Head-Shoulder Aided Multi-Person Tracker and a New Large-Scale Dataset for Tour-Guide RobotCode1
SiamMask: A Framework for Fast Online Object Tracking and SegmentationCode4
Parametric and Multivariate Uncertainty Calibration for Regression and Object DetectionCode2
Divert More Attention to Vision-Language Tracking—0
BoT-SORT: Robust Associations Multi-Pedestrian TrackingCode3
SRCN3D: Sparse R-CNN 3D for Compact Convolutional Multi-View 3D Object Detection and TrackingCode1
3D Multi-Object Tracking with Differentiable Pose Estimation—0
Warped Convolutional Networks: Bridge Homography to sl(3) algebra by Group Convolution—0
GNN-PMB: A Simple but Effective Online 3D Multi-Object Tracker without Bells and WhistlesCode1
Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy AutoencodersCode2
Real-time Full-stack Traffic Scene Perception for Autonomous Driving with Roadside Cameras—0
0/1 Deep Neural Networks via Block Coordinate Descent—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