Papers Multiple Object Tracking
“Multiple Object Tracking” 태그가 달린 논문 326편 · 필터 해제
COOLer: Class-Incremental Learning for Appearance-Based Multiple Object Tracking
Continual learning allows a model to learn multiple tasks sequentially while retaining the old knowledge without the training data of the preceding tasks. This paper extends the scope of continual learning research to cl…
class-incremental learningClass Incremental LearningContinual LearningDisentanglement+4DARTH: Holistic Test-time Adaptation for Multiple Object Tracking
Multiple object tracking (MOT) is a fundamental component of perception systems for autonomous driving, and its robustness to unseen conditions is a requirement to avoid life-critical failures. Despite the urge of safety…
Autonomous DrivingMultiple Object TrackingObjectobject-detection+33D Multiple Object Tracking on Autonomous Driving: A Literature Review
3D multi-object tracking (3D MOT) stands as a pivotal domain within autonomous driving, experiencing a surge in scholarly interest and commercial promise over recent years. Despite its paramount significance, 3D MOT conf…
3D Multi-Object TrackingAutonomous DrivingMulti-Object TrackingMultiple Object Tracking+1RaTrack: Moving Object Detection and Tracking with 4D Radar Point Cloud
Mobile autonomy relies on the precise perception of dynamic environments. Robustly tracking moving objects in 3D world thus plays a pivotal role for applications like trajectory prediction, obstacle avoidance, and path p…
Motion EstimationMotion SegmentationMoving Object DetectionMultiple Object Tracking+4Forensic Video Analytic Software
Law enforcement officials heavily depend on Forensic Video Analytic (FVA) Software in their evidence extraction process. However present-day FVA software are complex, time consuming, equipment dependent and expensive. De…
Activity RecognitionAnomaly DetectionGPUImage Enhancement+5SoccerNet 2023 Challenges Results
The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main t…
Action SpottingCamera CalibrationDense Video CaptioningJersey Number Recognition+4DeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions
Multiple object tracking (MOT) tends to become more challenging when severe occlusions occur. In this paper, we analyze the limitations of traditional Convolutional Neural Network-based methods and Transformer-based meth…
DecoderDenoisingMultiple Object TrackingObject+2FishMOT: A Simple and Effective Method for Fish Tracking Based on IoU Matching
Fish tracking plays a vital role in understanding fish behavior and ecology. However, existing tracking methods face challenges in accuracy and robustness dues to morphological change of fish, occlusion and complex envir…
Fish DetectionMulti-Object TrackingMultiple Object TrackingObject+3Object-Centric Multiple Object Tracking
Unsupervised object-centric learning methods allow the partitioning of scenes into entities without additional localization information and are excellent candidates for reducing the annotation burden of multiple-object t…
Multiple Object TrackingObjectobject-detectionObject Detection+2LEGO: Learning and Graph-Optimized Modular Tracker for Online Multi-Object Tracking with Point Clouds
Online multi-object tracking (MOT) plays a pivotal role in autonomous systems. The state-of-the-art approaches usually employ a tracking-by-detection method, and data association plays a critical role. This paper propose…
Multi-Object TrackingMultiple Object TrackingObjectObject Tracking+1AttMOT: Improving Multiple-Object Tracking by Introducing Auxiliary Pedestrian Attributes
Multi-object tracking (MOT) is a fundamental problem in computer vision with numerous applications, such as intelligent surveillance and automated driving. Despite the significant progress made in MOT, pedestrian attribu…
AttributeMulti-Object TrackingMultiple Object TrackingObject TrackingFOLT: Fast Multiple Object Tracking from UAV-captured Videos Based on Optical Flow
Multiple object tracking (MOT) has been successfully investigated in computer vision. However, MOT for the videos captured by unmanned aerial vehicles (UAV) is still challenging due to small object size, blurred object a…
motion predictionMultiple Object TrackingObjectobject-detection+3Hybrid-SORT: Weak Cues Matter for Online Multi-Object Tracking
Multi-Object Tracking (MOT) aims to detect and associate all desired objects across frames. Most methods accomplish the task by explicitly or implicitly leveraging strong cues (i.e., spatial and appearance information), …
Multi-Object TrackingMultiple Object TrackingObject TrackingOnline Multi-Object TrackingMeMOTR: Long-Term Memory-Augmented Transformer for Multi-Object Tracking
As a video task, Multiple Object Tracking (MOT) is expected to capture temporal information of targets effectively. Unfortunately, most existing methods only explicitly exploit the object features between adjacent frames…
Multi-Object TrackingMultiple Object TrackingObjectObject TrackingMotionTrack: End-to-End Transformer-based Multi-Object Tracing with LiDAR-Camera Fusion
Multiple Object Tracking (MOT) is crucial to autonomous vehicle perception. End-to-end transformer-based algorithms, which detect and track objects simultaneously, show great potential for the MOT task. However, most exi…
Autonomous DrivingMultiple Object TrackingObjectobject-detection+2Segmentation and Tracking of Vegetable Plants by Exploiting Vegetable Shape Feature for Precision Spray of Agricultural Robots
With the increasing deployment of agricultural robots, the traditional manual spray of liquid fertilizer and pesticide is gradually being replaced by agricultural robots. For robotic precision spray application in vegeta…
Instance SegmentationMultiple Object TrackingObject TrackingPlant Phenotyping+2Iterative 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+2UTOPIA: Unconstrained Tracking Objects without Preliminary Examination via Cross-Domain Adaptation
Multiple Object Tracking (MOT) aims to find bounding boxes and identities of targeted objects in consecutive video frames. While fully-supervised MOT methods have achieved high accuracy on existing datasets, they cannot …
Domain AdaptationMultiple Object TrackingObject TrackingSelf-Supervised LearningTracking Objects with 3D Representation from Videos
Data association is a knotty problem for 2D Multiple Object Tracking due to the object occlusion. However, in 3D space, data association is not so hard. Only with a 3D Kalman Filter, the online object tracker can associa…
Multiple Object TrackingObjectObject TrackingRepresentation LearningSparseTrack: 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 Tracking