Papers Multi-Object Tracking
“Multi-Object Tracking” 태그가 달린 논문 812편 · 필터 해제
Multi-Object Tracking as Attention Mechanism
We propose a conceptually simple and thus fast multi-object tracking (MOT) model that does not require any attached modules, such as the Kalman filter, Hungarian algorithm, transformer blocks, or graph networks. Conventi…
Multi-Object TrackingObjectObject TrackingHA-ViD: A Human Assembly Video Dataset for Comprehensive Assembly Knowledge Understanding
Understanding comprehensive assembly knowledge from videos is critical for futuristic ultra-intelligent industry. To enable technological breakthrough, we present HA-ViD - the first human assembly video dataset that feat…
Action RecognitionAction SegmentationMulti-Object TrackingObject+4Trajectory Poisson multi-Bernoulli mixture filter for traffic monitoring using a drone
This paper proposes a multi-object tracking (MOT) algorithm for traffic monitoring using a drone equipped with optical and thermal cameras. Object detections on the images are obtained using a neural network for each typ…
Multi-Object TrackingObjectObject Trackingparameter estimation+1On Gibbs Sampling Architecture for Labeled Random Finite Sets Multi-Object Tracking
Gibbs sampling is one of the most popular Markov chain Monte Carlo algorithms because of its simplicity, scalability, and wide applicability within many fields of statistics, science, and engineering. In the labeled rand…
Multi-Object TrackingObject TrackingIterative Scale-Up ExpansionIoU and Deep Features Association for Multi-Object Tracking in Sports
Deep learning-based object detectors have driven notable progress in multi-object tracking algorithms. Yet, current tracking methods mainly focus on simple, regular motion patterns in pedestrians or vehicles. This leaves…
Multi-Object TrackingMultiple Object TrackingObjectobject-detection+2Marking anything: application of point cloud in extracting video target features
Extracting retrievable features from video is of great significance for structured video database construction, video copyright protection and fake video rumor refutation. Inspired by point cloud data processing, this pa…
Multi-Object TrackingObject TrackingRetrievalTrajectoryFormer: 3D Object Tracking Transformer with Predictive Trajectory Hypotheses
3D multi-object tracking (MOT) is vital for many applications including autonomous driving vehicles and service robots. With the commonly used tracking-by-detection paradigm, 3D MOT has made important progress in recent …
3D Multi-Object Tracking3D Object TrackingAutonomous DrivingMulti-Object Tracking+2SparseTrack: Multi-Object Tracking by Performing Scene Decomposition based on Pseudo-Depth
Exploring robust and efficient association methods has always been an important issue in multiple-object tracking (MOT). Although existing tracking methods have achieved impressive performance, congestion and frequent oc…
Depth EstimationMulti-Object TrackingMultiple Object TrackingObject TrackingMotionTrack: Learning Motion Predictor for Multiple Object Tracking
Significant progress has been achieved in multi-object tracking (MOT) through the evolution of detection and re-identification (ReID) techniques. Despite these advancements, accurately tracking objects in scenarios with …
motion predictionMulti-Object TrackingMultiple Object TrackingObject+1Dictionary Learning under Symmetries via Group Representations
The dictionary learning problem can be viewed as a data-driven process to learn a suitable transformation so that data is sparsely represented directly from example data. In this paper, we examine the problem of learning…
Dictionary LearningDimensionality ReductionMulti-Object TrackingObject Tracking+1Z-GMOT: Zero-shot Generic Multiple Object Tracking
Despite recent significant progress, Multi-Object Tracking (MOT) faces limitations such as reliance on prior knowledge and predefined categories and struggles with unseen objects. To address these issues, Generic Multipl…
Multi-Object TrackingMultiple Object TrackingObjectObject TrackingMOTRv3: Release-Fetch Supervision for End-to-End Multi-Object Tracking
Although end-to-end multi-object trackers like MOTR enjoy the merits of simplicity, they suffer from the conflict between detection and association seriously, resulting in unsatisfactory convergence dynamics. While MOTRv…
DenoisingMulti-Object TrackingObject TrackingPseudo LabelBridging the Gap Between End-to-end and Non-End-to-end Multi-Object Tracking
Existing end-to-end Multi-Object Tracking (e2e-MOT) methods have not surpassed non-end-to-end tracking-by-detection methods. One potential reason is its label assignment strategy during training that consistently binds t…
Multi-Object TrackingObject TrackingVideo Object TrackingObject Re-Identification from Point Clouds
Object re-identification (ReID) from images plays a critical role in application domains of image retrieval (surveillance, retail analytics, etc.) and multi-object tracking (autonomous driving, robotics, etc.). However, …
3D Multi-Object TrackingAutonomous DrivingImage RetrievalMulti-Object Tracking+3SCTracker: Multi-object tracking with shape and confidence constraints
Detection-based tracking is one of the main methods of multi-object tracking. It can obtain good tracking results when using excellent detectors but it may associate wrong targets when facing overlapping and low-confiden…
Multi-Object TrackingObjectObject TrackingGeoMAE: Masked Geometric Target Prediction for Self-supervised Point Cloud Pre-Training
This paper tries to address a fundamental question in point cloud self-supervised learning: what is a good signal we should leverage to learn features from point clouds without annotations? To answer that, we introduce a…
DecoderMulti-Object Trackingobject-detectionObject Detection+4Non-Separable Multi-Dimensional Network Flows for Visual Computing
Flows in networks (or graphs) play a significant role in numerous computer vision tasks. The scalar-valued edges in these graphs often lead to a loss of information and thereby to limitations in terms of expressiveness. …
feature selectionMulti-Object TrackingObject TrackingSelf-Supervised Multi-Object Tracking For Autonomous Driving From Consistency Across Timescales
Self-supervised multi-object trackers have tremendous potential as they enable learning from raw domain-specific data. However, their re-identification accuracy still falls short compared to their supervised counterparts…
Autonomous DrivingMulti-Object TrackingObject TrackingSelf-Supervised LearningYou Only Need Two Detectors to Achieve Multi-Modal 3D Multi-Object Tracking
In the classical tracking-by-detection (TBD) paradigm, detection and tracking are separately and sequentially conducted, and data association must be properly performed to achieve satisfactory tracking performance. In th…
3D Multi-Object TrackingMulti-Object TrackingObjectobject-detection+3OVTrack: Open-Vocabulary Multiple Object Tracking
The ability to recognize, localize and track dynamic objects in a scene is fundamental to many real-world applications, such as self-driving and robotic systems. Yet, traditional multiple object tracking (MOT) benchmarks…
DenoisingHallucinationKnowledge DistillationMulti-Object Tracking+3