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 751–800 of 1966 papers

TitleStatusHype
Autoregressive Visual TrackingCode2
Resource-Efficient RGBD Aerial TrackingCode1
Tracking Passengers and Baggage Items using Multiple Overhead Cameras at Security CheckpointsCode0
High-temporal-resolution event-based vehicle detection and tracking—0
Beyond SOT: Tracking Multiple Generic Objects at Once—0
Tracking by Associating Clips—0
Detection-aware multi-object tracking evaluationCode0
Neural Enhanced Belief Propagation for Multiobject TrackingCode0
Learning a Fast 3D Spectral Approach to Object Segmentation and Tracking over Space and Time—0
Event-based Visual Tracking in Dynamic EnvironmentsCode0
Joint Counting, Detection and Re-Identification for Multi-Object Tracking—0
Multiple Object Tracking Challenge Technical Report for Team MT_IoTCode1
Sparse Message Passing Network with Feature Integration for Online Multiple Object Tracking—0
Generalizing Multiple Object Tracking to Unseen Domains by Introducing Natural Language Representation—0
CC-3DT: Panoramic 3D Object Tracking via Cross-Camera FusionCode2
Siamese Object Tracking for Vision-Based UAM Approaching with Pairwise Scale-Channel AttentionCode1
Improving Siamese Based Trackers with Light or No Training through Multiple Templates and Temporal Network—0
Hard to Track Objects with Irregular Motions and Similar Appearances? Make It Easier by Buffering the Matching SpaceCode1
Roboflow 100: A Rich, Multi-Domain Object Detection BenchmarkCode2
1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results—0
On Designing Light-Weight Object Trackers through Network Pruning: Use CNNs or Transformers?Code0
β-Multivariational Autoencoder for Entangled Representation Learning in Video FramesCode0
PVT++: A Simple End-to-End Latency-Aware Visual Tracking FrameworkCode1
Unifying Tracking and Image-Video Object Detection—0
GLT-T: Global-Local Transformer Voting for 3D Single Object Tracking in Point CloudsCode1
Revisiting Color-Event based Tracking: A Unified Network, Dataset, and MetricCode1
Multi-Camera Multi-Object Tracking on the Move via Single-Stage Global Association Approach—0
MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object DetectorsCode2
Robust Online Video Instance Segmentation with Track QueriesCode0
SMILEtrack: SiMIlarity LEarning for Occlusion-Aware Multiple Object TrackingCode2
Semantic keypoint extraction for scanned animals using multi-depth-camera systemsCode0
3D-SiamMask: Vision-Based Multi-Rotor Aerial-Vehicle Tracking for a Moving ObjectCode0
SportsTrack: An Innovative Method for Tracking Athletes in Sports ScenesCode1
CXTrack: Improving 3D Point Cloud Tracking with Contextual Information—0
Partial Binarization of Neural Networks for Budget-Aware Efficient Learning—0
NeighborTrack: Improving Single Object Tracking by Bipartite Matching with Neighbor TrackletsCode1
An Improved End-to-End Multi-Target Tracking Method Based on Transformer Self-Attention—0
Efficient Joint Detection and Multiple Object Tracking with Spatially Aware Transformer—0
MEVID: Multi-view Extended Videos with Identities for Video Person Re-IdentificationCode1
Estimation of Appearance and Occupancy Information in Birds Eye View from Surround Monocular Images—0
ShaSTA: Modeling Shape and Spatio-Temporal Affinities for 3D Multi-Object Tracking—0
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report—0
Learning Dual-Fused Modality-Aware Representations for RGBD Tracking—0
A Deep-Unfolded Spatiotemporal RPCA Network For L+S Decomposition—0
Development and evaluation of automated localisation and reconstruction of all fruits on tomato plants in a greenhouse based on multi-view perception and 3D multi-object tracking—0
Large Scale Real-World Multi-Person TrackingCode1
SearchTrack: Multiple Object Tracking with Object-Customized Search and Motion-Aware FeaturesCode1
TripletTrack: 3D Object Tracking using Triplet Embeddings and LSTM—0
ProContEXT: Exploring Progressive Context Transformer for TrackingCode1
End-to-end Tracking with a Multi-query Transformer—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