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

Showing 826850 of 1966 papers

TitleStatusHype
Interactive Multi-scale Fusion of 2D and 3D Features for Multi-object Tracking0
Learning Policies for Adaptive Tracking with Deep Feature Cascades0
Interpretable Deep Tracking0
InterTracker: Discovering and Tracking General Objects Interacting with Hands in the Wild0
InterTrack: Interaction Transformer for 3D Multi-Object Tracking0
Learning The Sequential Temporal Information with Recurrent Neural Networks0
Globally Optimal Object Tracking with Fully Convolutional Networks0
Global Correlation Network: End-to-End Joint Multi-Object Detection and Tracking0
GLAN: A Graph-based Linear Assignment Network0
CorrBEV: Multi-View 3D Object Detection by Correlation Learning with Multi-modal Prototypes0
IP-MOT: Instance Prompt Learning for Cross-Domain Multi-Object Tracking0
ISAR: A Benchmark for Single- and Few-Shot Object Instance Segmentation and Re-Identification0
Is First Person Vision Challenging for Object Tracking?0
Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood Maps0
Is Multiple Object Tracking a Matter of Specialization?0
Isometric Multi-Shape Matching0
Learning Moving-Object Tracking with FMCW LiDAR0
GeoWATCH for Detecting Heavy Construction in Heterogeneous Time Series of Satellite Images0
Coreset-Based Adaptive Tracking0
Generating Synthetic Training Data for Deep Learning-Based UAV Trajectory Prediction0
Joint 3D Reconstruction of a Static Scene and Moving Objects0
Generating superpixels using deep image representations0
Attention-Aware Multi-View Pedestrian Tracking0
Learning Multi-Object Tracking and Segmentation from Automatic Annotations0
Aerial multi-object tracking by detection using deep association networks0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HR-CEUTrack-LargeSuccess Rate65Unverified
2HR-CEUTrack-BaseSuccess Rate63.2Unverified
3CEUTrack-LargeSuccess Rate62.8Unverified
4CEUTrack-BaseSuccess Rate62Unverified
5SiamR-CNNSuccess Rate60.9Unverified
6TransTSuccess Rate60.5Unverified
7SuperDiMPSuccess Rate60.2Unverified
8TrDiMPSuccess Rate60.1Unverified
9KeepTrackSuccess Rate59.6Unverified
10AiATrackSuccess Rate59Unverified
#ModelMetricClaimedVerifiedStatus
1HR-MonTrack-BaseSuccess Rate68.5Unverified
2HR-MonTrack-TinySuccess Rate66.3Unverified
3Multi-modalSuccess Rate63.4Unverified
4PrDiMPSuccess Rate59Unverified
5DiMPSuccess Rate57.1Unverified
6MonTrackSuccess Rate54.9Unverified
7ATOMSuccess Rate46.5Unverified
8KYSSuccess Rate26.6Unverified
#ModelMetricClaimedVerifiedStatus
1OmniTrackHOTA23.45Unverified
2DeepSORTHOTA21.16Unverified
3OC-SORTHOTA20.83Unverified
4ByteTrackHOTA20.66Unverified
5TrackFormerHOTA19.62Unverified
6HybridSORTHOTA16.64Unverified
7DiffMOTHOTA16.4Unverified
8Bot-SORTHOTA15.77Unverified
#ModelMetricClaimedVerifiedStatus
1DiMP50Success Rate67.33Unverified
2PrDiMP50Success Rate67Unverified
3PrDiMP18Success Rate65.9Unverified
4DiMP18Success Rate64.6Unverified
5AtomSuccess Rate63.8Unverified
#ModelMetricClaimedVerifiedStatus
1finalHumans0.14Unverified
2night_furyHumans0.05Unverified
3Yolo based methodHumans0.02Unverified
4finalHumans0Unverified
#ModelMetricClaimedVerifiedStatus
1M2-Trackmean precision83.4Unverified
2BATmean precision75.2Unverified
#ModelMetricClaimedVerifiedStatus
1UMMT3DMOTA95Unverified
2MMPTRACK3DMOTA94.8Unverified
#ModelMetricClaimedVerifiedStatus
1Siam-FCAverage IOU0.66Unverified
#ModelMetricClaimedVerifiedStatus
1RT-MDNetPrecision Plot0.63Unverified