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.
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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.