MVDGC unifies BEV and image-view pedestrian localization into one task via 3D cylindrical queries that enforce dual geometric constraints between views.
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BoT-SORT: Robust as- sociations multi-pedestrian tracking,
28 Pith papers cite this work. Polarity classification is still indexing.
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CityOS is an edge runtime that enforces a three-tier privacy API for urban sensors: local raw data, differentially private single-location stats, and cross-location aggregates with per-user budgets enforced on devices.
CROWD is a new global dataset of 51,753 continuous urban dashcam segments spanning over 20,000 hours from 238 countries, with manual labels and automated object detections for routine driving analysis.
CylindTrack improves identity preservation in panoramic multi-object tracking by combining depth-temporal trajectory modeling, spherical spatio-temporal consistency learning, and topology-aware cylindrical motion prediction.
Presents a new egocentric HRI dataset and reports that an optimized tracking pipeline with ReID reduces identity switches by 49% over baseline.
GateMOT proposes Q-Gated Attention to enable linear-complexity, spatially aware attention for state-of-the-art dense object tracking on benchmarks like BEE24.
ART-Track is a motion-driven multi-object tracker that reduces identity switches in low-quality microgravity videos of model organisms by combining multi-model motion estimation, state-driven association, and uncertainty-adaptive cue fusion.
WildLIFT lifts monocular drone video to 3D for species-agnostic wildlife detection, tracking, and viewpoint analysis by integrating scene geometry with open-vocabulary segmentation.
SPL unifies unsupervised and sparsely-supervised 3D object detection via semantic pseudo-labeling that produces bounding boxes and point labels, followed by memory-based prototype learning that mines features from both labeled and unlabeled data.
EASE-MCVT is a distributed edge-assisted multi-camera vehicle tracking framework that achieves real-time performance and competitive accuracy on public datasets through edge processing and server-side optimizations.
OmniTrack++ improves omnidirectional multi-object tracking with trajectory feedback through DynamicSSM stabilization, FlexiTrack instances, ExpertTrack Memory with Mixture-of-Experts, and adaptive Tracklet Management, achieving SOTA HOTA gains on JRDB and new EmboTrack benchmark.
Introduces TimberVision dataset and multi-task framework for log-component segmentation, detection, and tracking in forestry operations using RGB images.
PS-Track sets a new state-of-the-art for point-supervised multi-object tracking by converting point seeds into temporally consistent pseudo-labels via Temporal-Feedback Prompting, Point-Excited Wavelet Attention, and Uncertainty-Guided Gaussian Learning.
Edge-TSR shows benchmark evaluations overestimate real-world edge inference performance by 20-30% and uses temporal stabilization to recover up to 10.16% classification accuracy in sustained roadside perception deployments.
IMPose introduces dual-level (keypoint and instance) correction propagation with a trajectory bank to turn sparse annotations into dense multi-person pose trajectories in videos.
SMAC introduces a spatial-modal fusion backbone and adaptive collapse network for multimodal MOT, reporting 63.31 HOTA and 79.21 MOTA on UniRTL RNT modality.
A three-stage pipeline applies YOLO11 detection, SAM segmentation, and persona-scaffolded adversarial chain-of-thought prompting on Qwen3-VL to monitor construction safety violations, reporting a 12% precision gain from the prompting method in an informal review.
SAMOFT improves multi-object tracking by using SAM segmentation and optical flow for pixel-level motion matching, flexible centroid correction, and training-free motion pattern fixes on top of standard Kalman and ReID baselines.
TCMP achieves SOTA MOT metrics (HOTA 63.4%, IDF1 65.0%, AssA 49.1%) with 0.014x parameters and 0.05x FLOPs of the previous best method by using a simple dilated TCN regressor.
HyperSSM integrates hypergraphs and state space models to let correlated objects mutually refine motion estimates, stabilizing trajectories under noise and occlusion for state-of-the-art multi-object tracking.
The paper derives an occlusion-aware multi-object tracking method that assigns each object an expected detection probability over the reduced Palm density within a multi-Bernoulli mixture filter.
NOOUGAT unifies online and offline multi-object tracking with a GNN that processes non-overlapping subclips fused by an Autoregressive Long-term Tracking layer, reporting SOTA gains on DanceTrack, SportsMOT, and MOT20.
Zero-shot VLM semantic descriptions achieve re-identification retrieval performance comparable to a supervised CNN baseline in autonomous driving but encounter attribute inconsistency across viewpoints.
Applying multi-object tracking to fuse softmax probabilities across frames in camera trap data yields weighted F1-score gains of 5.1%, 3.1%, and 2.0% over standalone classifiers on three datasets.
citing papers explorer
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MVDGC: Joint 3D and 2D Multi-view Pedestrian Detection via Dual Geometric Constraints
MVDGC unifies BEV and image-view pedestrian localization into one task via 3D cylindrical queries that enforce dual geometric constraints between views.
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CityOS: Privacy Architecture for Urban Sensing
CityOS is an edge runtime that enforces a three-tier privacy API for urban sensors: local raw data, differentially private single-location stats, and cross-location aggregates with per-user budgets enforced on devices.
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A global dataset of continuous urban dashcam driving
CROWD is a new global dataset of 51,753 continuous urban dashcam segments spanning over 20,000 hours from 238 countries, with manual labels and automated object detections for routine driving analysis.
-
CylindTrack: Depth-Aware Cylindrical Motion Modeling for Panoramic Multi-Object Tracking
CylindTrack improves identity preservation in panoramic multi-object tracking by combining depth-temporal trajectory modeling, spherical spatio-temporal consistency learning, and topology-aware cylindrical motion prediction.
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Face versus Body Tracking for Human-Robot Interaction: An Egocentric Dataset
Presents a new egocentric HRI dataset and reports that an optimized tracking pipeline with ReID reduces identity switches by 49% over baseline.
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GateMOT: Q-Gated Attention for Dense Object Tracking
GateMOT proposes Q-Gated Attention to enable linear-complexity, spatially aware attention for state-of-the-art dense object tracking on benchmarks like BEE24.
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Motion-Driven Multi-Object Tracking of Model Organisms in Space Science Experiments
ART-Track is a motion-driven multi-object tracker that reduces identity switches in low-quality microgravity videos of model organisms by combining multi-model motion estimation, state-driven association, and uncertainty-adaptive cue fusion.
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WildLIFT: Lifting monocular drone video to 3D for species-agnostic wildlife monitoring
WildLIFT lifts monocular drone video to 3D for species-agnostic wildlife detection, tracking, and viewpoint analysis by integrating scene geometry with open-vocabulary segmentation.
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Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning
SPL unifies unsupervised and sparsely-supervised 3D object detection via semantic pseudo-labeling that produces bounding boxes and point labels, followed by memory-based prototype learning that mines features from both labeled and unlabeled data.
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Edge Assisted Multi-Camera Vehicle Tracking Framework for Real-Time and Scalable Deployment
EASE-MCVT is a distributed edge-assisted multi-camera vehicle tracking framework that achieves real-time performance and competitive accuracy on public datasets through edge processing and server-side optimizations.
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OmniTrack++: Omnidirectional Multi-Object Tracking by Learning Large-FoV Trajectory Feedback
OmniTrack++ improves omnidirectional multi-object tracking with trajectory feedback through DynamicSSM stabilization, FlexiTrack instances, ExpertTrack Memory with Mixture-of-Experts, and adaptive Tracklet Management, achieving SOTA HOTA gains on JRDB and new EmboTrack benchmark.
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TimberVision: A Multi-Task Dataset and Framework for Log-Component Segmentation and Tracking in Autonomous Forestry Operations
Introduces TimberVision dataset and multi-task framework for log-component segmentation, detection, and tracking in forestry operations using RGB images.
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PS-MOT: Cultivating Instance Awareness from Point Seeds for Multi-Object Tracking
PS-Track sets a new state-of-the-art for point-supervised multi-object tracking by converting point seeds into temporally consistent pseudo-labels via Temporal-Feedback Prompting, Point-Excited Wavelet Attention, and Uncertainty-Guided Gaussian Learning.
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Beyond Benchmarks: Continuous Edge Inference for Fine-Grained Roadside Perception
Edge-TSR shows benchmark evaluations overestimate real-world edge inference performance by 20-30% and uses temporal stabilization to recover up to 10.16% classification accuracy in sustained roadside perception deployments.
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IMPose: Interactive Multi-person Pose Estimation with Dynamic Correction Propagation
IMPose introduces dual-level (keypoint and instance) correction propagation with a trajectory bank to turn sparse annotations into dense multi-person pose trajectories in videos.
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SMAC: Spatial-Modal Joint Modeling and Adaptive Representation Collapse for Multimodal Object Tracking
SMAC introduces a spatial-modal fusion backbone and adaptive collapse network for multimodal MOT, reporting 63.31 HOTA and 79.21 MOTA on UniRTL RNT modality.
-
Passive Construction Site Safety Monitoring via Persona-Scaffolded Adversarial Chain-of-Thought VLM Verification
A three-stage pipeline applies YOLO11 detection, SAM segmentation, and persona-scaffolded adversarial chain-of-thought prompting on Qwen3-VL to monitor construction safety violations, reporting a 12% precision gain from the prompting method in an informal review.
-
SAMOFT: Robust Multi-Object Tracking via Region and Flow
SAMOFT improves multi-object tracking by using SAM segmentation and optical flow for pixel-level motion matching, flexible centroid correction, and training-free motion pattern fixes on top of standard Kalman and ReID baselines.
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Time-series Meets Complex Motion Modeling: Robust and Computational-effective Motion Predictor for Multi-object Tracking
TCMP achieves SOTA MOT metrics (HOTA 63.4%, IDF1 65.0%, AssA 49.1%) with 0.014x parameters and 0.05x FLOPs of the previous best method by using a simple dilated TCN regressor.
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Hypergraph-State Collaborative Reasoning for Multi-Object Tracking
HyperSSM integrates hypergraphs and state space models to let correlated objects mutually refine motion estimates, stabilizing trajectories under noise and occlusion for state-of-the-art multi-object tracking.
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Occlusion-Aware Multi-Object Tracking via Expected Probability of Detection
The paper derives an occlusion-aware multi-object tracking method that assigns each object an expected detection probability over the reduced Palm density within a multi-Bernoulli mixture filter.
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NOOUGAT: Towards Unified Online and Offline Multi-Object Tracking
NOOUGAT unifies online and offline multi-object tracking with a GNN that processes non-overlapping subclips fused by an Autoregressive Long-term Tracking layer, reporting SOTA gains on DanceTrack, SportsMOT, and MOT20.
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Zero-Shot Semantic Re-Identification for Autonomous Driving: A VLM Baseline Study
Zero-shot VLM semantic descriptions achieve re-identification retrieval performance comparable to a supervised CNN baseline in autonomous driving but encounter attribute inconsistency across viewpoints.
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Multi-Object Tracking Consistently Improves Wildlife Inference
Applying multi-object tracking to fuse softmax probabilities across frames in camera trap data yields weighted F1-score gains of 5.1%, 3.1%, and 2.0% over standalone classifiers on three datasets.
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OpenPodcar2: a robust, ROS2 vehicle for self-driving research
OpenPodcar2 is a low-cost, open-source ROS2 autonomous vehicle platform built from a mobility scooter, with hardware build instructions, Gazebo simulation, and nav2-based planning for research and limited deployment.
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EgoLive: A Large-Scale Egocentric Dataset from Real-World Human Tasks
EgoLive is presented as the largest open-source annotated egocentric dataset for real-world task-oriented human routines, captured with a custom head-mounted device and multi-modal annotations exclusively in unconstrained environments.
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SAMIDARE: Advanced Tracking-by-Segmentation for Dense Scenarios
SAMIDARE improves segmentation-based multi-object tracking in dense sports videos, gaining 2.5 HOTA and 4.2 IDF1 over baseline on SportsMOT validation through adaptive mask control and state-aware association.
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Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions
A survey of trajectory prediction techniques for autonomous vehicles that proposes a taxonomy, overviews the prediction pipeline, and highlights remaining research gaps.