Papers Visual Object Tracking
“Visual Object Tracking” 태그가 달린 논문 369편 · 필터 해제
Distractor-Aware Memory-Based Visual Object Tracking
Recent emergence of memory-based video segmentation methods such as SAM2 has led to models with excellent performance in segmentation tasks, achieving leading results on numerous benchmarks. However, these modes are not …
Visual Object TrackingVideo SegmentationISTASTrack: Bridging ANN and SNN via ISTA Adapter for RGB-Event Tracking
RGB-Event tracking has become a promising trend in visual object tracking to leverage the complementary strengths of both RGB images and dynamic spike events for improved performance. However, existing artificial neural …
Visual Object TrackingVisual TrackingOmni Survey for Multimodality Analysis in Visual Object Tracking
The development of smart cities has led to the generation of massive amounts of multi-modal data in the context of a range of tasks that enable a comprehensive monitoring of the smart city infrastructure and services. Th…
Visual Object TrackingSerial Over Parallel: Learning Continual Unification for Multi-Modal Visual Object Tracking and Benchmarking
Unifying multiple multi-modal visual object tracking (MMVOT) tasks draws increasing attention due to the complementary nature of different modalities in building robust tracking systems. Existing practices mix all data s…
Visual Object TrackingContinual LearningSAMITE: Position Prompted SAM2 with Calibrated Memory for Visual Object Tracking
Visual Object Tracking (VOT) is widely used in applications like autonomous driving to continuously track targets in videos. Existing methods can be roughly categorized into template matching and autoregressive methods, …
Visual Object TrackingAutonomous DrivingIs Tracking really more challenging in First Person Egocentric Vision?
Visual object tracking and segmentation are becoming fundamental tasks for understanding human activities in egocentric vision. Recent research has benchmarked state-of-the-art methods and concluded that first person ego…
Visual Object TrackingSAM2RL: Towards Reinforcement Learning Memory Control in Segment Anything Model 2
Segment Anything Model 2 (SAM 2) has demonstrated strong performance in object segmentation tasks and has become the state-of-the-art for visual object tracking. The model stores information from previous frames in a mem…
Reinforcement LearningVisual Object TrackingObject SegmentationTemporal Unlearnable Examples: Preventing Personal Video Data from Unauthorized Exploitation by Object Tracking
With the rise of social media, vast amounts of user-uploaded videos (e.g., YouTube) are utilized as training data for Visual Object Tracking (VOT). However, the VOT community has largely overlooked video data-privacy iss…
Visual Object TrackingImage ClassificationUMDATrack: Unified Multi-Domain Adaptive Tracking Under Adverse Weather Conditions
Visual object tracking has gained promising progress in past decades. Most of the existing approaches focus on learning target representation in well-conditioned daytime data, while for the unconstrained real-world scena…
Domain AdaptationObject TrackingVisual Object TrackingMamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking
Combining traditional RGB cameras with bio-inspired event cameras for robust object tracking has garnered increasing attention in recent years. However, most existing multimodal tracking algorithms depend heavily on high…
MambaObject TrackingVisual Object TrackingR1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning
Visual single object tracking aims to continuously localize and estimate the scale of a target in subsequent video frames, given only its initial state in the first frame. This task has traditionally been framed as a tem…
Object TrackingTemplate MatchingVisual Object TrackingVisual TrackingFully Spiking Neural Networks for Unified Frame-Event Object Tracking
The integration of image and event streams offers a promising approach for achieving robust visual object tracking in complex environments. However, current fusion methods achieve high performance at the cost of signific…
Object TrackingVisual Object TrackingProgressive Scaling Visual Object Tracking
In this work, we propose a progressive scaling training strategy for visual object tracking, systematically analyzing the influence of training data volume, model size, and input resolution on tracking performance. Our e…
ObjectObject TrackingVisual Object TrackingAdapting SAM 2 for Visual Object Tracking: 1st Place Solution for MMVPR Challenge Multi-Modal Tracking
We present an effective approach for adapting the Segment Anything Model 2 (SAM2) to the Visual Object Tracking (VOT) task. Our method leverages the powerful pre-trained capabilities of SAM2 and incorporates several key …
ObjectObject TrackingVisual Object TrackingTowards Low-Latency Event Stream-based Visual Object Tracking: A Slow-Fast Approach
Existing tracking algorithms typically rely on low-frame-rate RGB cameras coupled with computationally intensive deep neural network architectures to achieve effective tracking. However, such frame-based methods inherent…
Knowledge DistillationObject TrackingVisual Object TrackingCGTrack: Cascade Gating Network with Hierarchical Feature Aggregation for UAV Tracking
Recent advancements in visual object tracking have markedly improved the capabilities of unmanned aerial vehicle (UAV) tracking, which is a critical component in real-world robotics applications. While the integration of…
Object TrackingVisual Object TrackingAdversarial Attack for RGB-Event based Visual Object Tracking
Visual object tracking is a crucial research topic in the fields of computer vision and multi-modal fusion. Among various approaches, robust visual tracking that combines RGB frames with Event streams has attracted incre…
Adversarial AttackObject TrackingVisual Object TrackingVisual TrackingSPMTrack: Spatio-Temporal Parameter-Efficient Fine-Tuning with Mixture of Experts for Scalable Visual Tracking
Most state-of-the-art trackers adopt one-stream paradigm, using a single Vision Transformer for joint feature extraction and relation modeling of template and search region images. However, relation modeling between diff…
Mixture-of-Expertsparameter-efficient fine-tuningRelationVisual Object Tracking+1UncTrack: Reliable Visual Object Tracking with Uncertainty-Aware Prototype Memory Network
Transformer-based trackers have achieved promising success and become the dominant tracking paradigm due to their accuracy and efficiency. Despite the substantial progress, most of the existing approaches tackle object t…
Object TrackingVisual Object TrackingMITracker: Multi-View Integration for Visual Object Tracking
Multi-view object tracking (MVOT) offers promising solutions to challenges such as occlusion and target loss, which are common in traditional single-view tracking. However, progress has been limited by the lack of compre…
ObjectObject TrackingVisual Object Tracking