Papers Object Tracking
“Object Tracking” 태그가 달린 논문 2,087편 · 필터 해제
INTACT: Inducing Noise Tolerance through Adversarial Curriculum Training for LiDAR-based Safety-Critical Perception and Autonomy
In this work, we present INTACT, a novel two-phase framework designed to enhance the robustness of deep neural networks (DNNs) against noisy LiDAR data in safety-critical perception tasks. INTACT combines meta-learning w…
Meta-LearningMultiple Object TrackingObject TrackingRethinking Vision Transformer for Object Centric Foundation Models
Recent state-of-the-art object segmentation mechanisms, such as the Segment Anything Model (SAM) and FastSAM, first encode the full image over several layers and then focus on generating the mask for one particular objec…
ObjectObject TrackingSegmentationSemantic SegmentationOvercoming Semantic Dilution in Transformer-Based Next Frame Prediction
Next-frame prediction in videos is crucial for applications such as autonomous driving, object tracking, and motion prediction. The primary challenge in next-frame prediction lies in effectively capturing and processing …
Autonomous Drivingmotion predictionObject TrackingPredictionUnderstanding Long Videos via LLM-Powered Entity Relation Graphs
The analysis of extended video content poses unique challenges in artificial intelligence, particularly when dealing with the complexity of tracking and understanding visual elements across time. Current methodologies th…
EgoSchemaLarge Language ModelObject TrackingRelation+1Visual Localization via Semantic Structures in Autonomous Photovoltaic Power Plant Inspection
Inspection systems utilizing unmanned aerial vehicles (UAVs) equipped with thermal cameras are increasingly popular for the maintenance of photovoltaic (PV) power plants. However, automation of the inspection task is a c…
Object TrackingVisual LocalizationYOLO11-JDE: Fast and Accurate Multi-Object Tracking with Self-Supervised Re-ID
We introduce YOLO11-JDE, a fast and accurate multi-object tracking (MOT) solution that combines real-time object detection with self-supervised Re-Identification (Re-ID). By incorporating a dedicated Re-ID branch into YO…
Multi-Object Trackingobject-detectionObject DetectionObject Tracking+2CSAOT: Cooperative Multi-Agent System for Active Object Tracking
Object Tracking is essential for many computer vision applications, such as autonomous navigation, surveillance, and robotics. Unlike Passive Object Tracking (POT), which relies on static camera viewpoints to detect and …
Autonomous NavigationDeep Reinforcement LearningMixture-of-ExpertsObject+1InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling
This paper aims to improve the performance of video multimodal large language models (MLLM) via long and rich context (LRC) modeling. As a result, we develop a new version of InternVideo2.5 with a focus on enhancing the …
Object TrackingReferring Expression SegmentationReferring Video Object SegmentationVideo UnderstandingPD-SORT: Occlusion-Robust Multi-Object Tracking Using Pseudo-Depth Cues
Multi-object tracking (MOT) is a rising topic in video processing technologies and has important application value in consumer electronics. Currently, tracking-by-detection (TBD) is the dominant paradigm for MOT, which p…
Motion CompensationMulti-Object TrackingObject TrackingSpatio-temporal Graph Learning on Adaptive Mined Key Frames for High-performance Multi-Object Tracking
In the realm of multi-object tracking, the challenge of accurately capturing the spatial and temporal relationships between objects in video sequences remains a significant hurdle. This is further complicated by frequent…
Graph LearningMulti-Object TrackingObject TrackingSST-EM: Advanced Metrics for Evaluating Semantic, Spatial and Temporal Aspects in Video Editing
Video editing models have advanced significantly, but evaluating their performance remains challenging. Traditional metrics, such as CLIP text and image scores, often fall short: text scores are limited by inadequate tra…
Objectobject-detectionObject DetectionObject Tracking+1TimberVision: A Multi-Task Dataset and Framework for Log-Component Segmentation and Tracking in Autonomous Forestry Operations
Timber represents an increasingly valuable and versatile resource. However, forestry operations such as harvesting, handling and measuring logs still require substantial human labor in remote environments posing signific…
BenchmarkingDomain AdaptationInstance SegmentationMulti-Object Tracking+6Robust Single Object Tracking in LiDAR Point Clouds under Adverse Weather Conditions
3D single object tracking (3DSOT) in LiDAR point clouds is a critical task for outdoor perception, enabling real-time perception of object location, orientation, and motion. Despite the impressive performance of current …
3D Single Object TrackingContrastive LearningObject TrackingAn Empirical Study of Autoregressive Pre-training from Videos
We empirically study autoregressive pre-training from videos. To perform our study, we construct a series of autoregressive video models, called Toto. We treat videos as sequences of visual tokens and train transformer m…
Object TrackingVideo ClassificationBuilding a Mind Palace: Structuring Environment-Grounded Semantic Graphs for Effective Long Video Analysis with LLMs
Long-form video understanding with Large Vision Language Models is challenged by the need to analyze temporally dispersed yet spatially concentrated key moments within limited context windows. In this work, we introduce …
EgoSchemaObject TrackingVideo UnderstandingNeuromorphic Optical Tracking and Imaging of Randomly Moving Targets through Strongly Scattering Media
Tracking and acquiring simultaneous optical images of randomly moving targets obscured by scattering media remains a challenging problem of importance to many applications that require precise object localization and ide…
Computational EfficiencyImage ReconstructionObject LocalizationObject TrackingDeTrack: In-model Latent Denoising Learning for Visual Object Tracking
Previous visual object tracking methods employ image-feature regression models or coordinate autoregression models for bounding box prediction. Image-feature regression methods heavily depend on matching results and do n…
DenoisingObject TrackingVisual Object TrackingHybridTrack: A Hybrid Approach for Robust Multi-Object Tracking
The evolution of Advanced Driver Assistance Systems (ADAS) has increased the need for robust and generalizable algorithms for multi-object tracking. Traditional statistical model-based tracking methods rely on predefined…
3D Multi-Object TrackingMulti-Object TrackingObject TrackingSensitivity of Room Impulse Responses in Changing Acoustic Environment
Changes in room acoustics, such as modifications to surface absorption or the insertion of a scattering object, significantly impact measured room impulse responses (RIRs). These changes can affect the performance of sys…
Object TrackingSensitivityLess is More: Token Context-aware Learning for Object Tracking
Recently, several studies have shown that utilizing contextual information to perceive target states is crucial for object tracking. They typically capture context by incorporating multiple video frames. However, these n…
Object TrackingVisual Tracking