Papers Object Tracking
“Object Tracking” 태그가 달린 논문 2,087편 · 필터 해제
MAML MOT: Multiple Object Tracking based on Meta-Learning
With the advancement of video analysis technology, the multi-object tracking (MOT) problem in complex scenes involving pedestrians is gaining increasing importance. This challenge primarily involves two key tasks: pedest…
Meta-LearningMulti-Object TrackingMultiple Object TrackingObject+2Multi-Object Tracking in the Dark
Low-light scenes are prevalent in real-world applications (e.g. autonomous driving and surveillance at night). Recently, multi-object tracking in various practical use cases have received much attention, but multi-object…
Autonomous DrivingMulti-Object TrackingObjectObject TrackingCommon Corruptions for Enhancing and Evaluating Robustness in Air-to-Air Visual Object Detection
The main barrier to achieving fully autonomous flights lies in autonomous aircraft navigation. Managing non-cooperative traffic presents the most important challenge in this problem. The most efficient strategy for handl…
Objectobject-detectionObject DetectionObject TrackingEnsuring UAV Safety: A Vision-only and Real-time Framework for Collision Avoidance Through Object Detection, Tracking, and Distance Estimation
In the last twenty years, unmanned aerial vehicles (UAVs) have garnered growing interest due to their expanding applications in both military and civilian domains. Detecting non-cooperative aerial vehicles with efficienc…
Collision AvoidanceDepth EstimationImage-to-Image TranslationNavigate+4Outlier-robust Kalman Filtering through Generalised Bayes
We derive a novel, provably robust, and closed-form Bayesian update rule for online filtering in state-space models in the presence of outliers and misspecified measurement models. Our method combines generalised Bayesia…
Bayesian InferenceComputational EfficiencyObject TrackingState Estimation+1TENet: Targetness Entanglement Incorporating with Multi-Scale Pooling and Mutually-Guided Fusion for RGB-E Object Tracking
There is currently strong interest in improving visual object tracking by augmenting the RGB modality with the output of a visual event camera that is particularly informative about the scene motion. However, existing ap…
Object TrackingVisual Object TrackingDriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving
Vision-centric autonomous driving has recently raised wide attention due to its lower cost. Pre-training is essential for extracting a universal representation. However, current vision-centric pre-training typically reli…
3D Object DetectionAutonomous DrivingMotion ForecastingMulti-Object Tracking+5Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning
We apply multi-agent deep reinforcement learning (RL) to train end-to-end robot soccer policies with fully onboard computation and sensing via egocentric RGB vision. This setting reflects many challenges of real-world ro…
Deep Reinforcement LearningObject Trackingreinforcement-learningReinforcement Learning+13D Extended Object Tracking by Fusing Roadside Sparse Radar Point Clouds and Pixel Keypoints
Roadside perception is a key component in intelligent transportation systems. In this paper, we present a novel three-dimensional (3D) extended object tracking (EOT) method, which simultaneously estimates the object kine…
Object Tracking360VOTS: Visual Object Tracking and Segmentation in Omnidirectional Videos
Visual object tracking and segmentation in omnidirectional videos are challenging due to the wide field-of-view and large spherical distortion brought by 360{\deg} images. To alleviate these problems, we introduce a nove…
ObjectObject TrackingSegmentationSemantic Segmentation+3TeamTrack: A Dataset for Multi-Sport Multi-Object Tracking in Full-pitch Videos
Multi-object tracking (MOT) is a critical and challenging task in computer vision, particularly in situations involving objects with similar appearances but diverse movements, as seen in team sports. Current methods, lar…
BenchmarkingMulti-Object Trackingobject-detectionObject Detection+1Inverse Neural Rendering for Explainable Multi-Object Tracking
Today, most methods for image understanding tasks rely on feed-forward neural networks. While this approach has allowed for empirical accuracy, efficiency, and task adaptation via fine-tuning, it also comes with fundamen…
3D Multi-Object TrackingInverse RenderingMulti-Object TrackingNeural Rendering+2MLS-Track: Multilevel Semantic Interaction in RMOT
The new trend in multi-object tracking task is to track objects of interest using natural language. However, the scarcity of paired prompt-instance data hinders its progress. To address this challenge, we propose a high-…
Multi-Object TrackingObject TrackingKnotResolver: Tracking self-intersecting filaments in microscopy using directed graphs
Quantification of microscopy time-series of in vitro reconstituted motor driven microtubule (MT) transport in 'gliding assays' is typically performed using computational object tracking tools. However, these are limited …
Contour DetectionObject TrackingTime SeriesHow to deal with glare for improved perception of Autonomous Vehicles
Vision sensors are versatile and can capture a wide range of visual cues, such as color, texture, shape, and depth. This versatility, along with the relatively inexpensive availability of machine vision cameras, played a…
Autonomous DrivingAutonomous VehiclesDepth EstimationLane Detection+5BoostTrack: boosting the similarity measure and detection confidence for improved multiple object tracking
Handling unreliable detections and avoiding identity switches are crucial for the success of multiple object tracking (MOT). Ideally, MOT algorithm should use true positive detections only, work in real-time and produce …
Motion CompensationMulti-Object TrackingMultiple Object TrackingMultiple People Tracking+1Into the Fog: Evaluating Robustness of Multiple Object Tracking
State-of-the-art Multiple Object Tracking (MOT) approaches have shown remarkable performance when trained and evaluated on current benchmarks. However, these benchmarks primarily consist of clear weather scenarios, overl…
Depth EstimationMonocular Depth EstimationMultiple Object TrackingObject+1Gaga: Group Any Gaussians via 3D-aware Memory Bank
We introduce Gaga, a framework that reconstructs and segments open-world 3D scenes by leveraging inconsistent 2D masks predicted by zero-shot class-agnostic segmentation models. Contrasted to prior 3D scene segmentation …
Contrastive LearningObject TrackingScene SegmentationScene Understanding+3SFSORT: Scene Features-based Simple Online Real-Time Tracker
This paper introduces SFSORT, the world's fastest multi-object tracking system based on experiments conducted on MOT Challenge datasets. To achieve an accurate and computationally efficient tracker, this paper employs a …
CPUMulti-Object TrackingObjectobject-detection+4PillarTrack:Boosting Pillar Representation for Transformer-based 3D Single Object Tracking on Point Clouds
LiDAR-based 3D single object tracking (3D SOT) is a critical issue in robotics and autonomous driving. Existing 3D SOT methods typically adhere to a point-based processing pipeline, wherein the re-sampling operation inva…
3D Single Object TrackingAutonomous DrivingObject Tracking