Skip to content
RobotWorld
← Tags

#Object tracking (5)

WaterfallPagination
Ultralytics multi-object tracker comparison
0:16

Ultralytics multi-object tracker comparison

@Alacritic_Super · 205 views · 2026-09-02
UltralyticsOCSORTDeepOCSORT
McByte tracker integrated into Roboflow
0:03

McByte tracker integrated into Roboflow

@mirrash7 · 76 views · 2026-08-12
McByteRoboflowassociation
QueenVIS: Prompt-Free Video Object Detection and Tracking
0:09

QueenVIS: Prompt-Free Video Object Detection and Tracking

@HuggingApps · 74 views · 2026-07-31
Object trackingObject DetectionQueenVIS
ReferTrack: Referring Then Tracking for Embodied Visual Tracking

ReferTrack: Referring Then Tracking for Embodied Visual Tracking

Embodied visual tracking (EVT) requires a mobile agent to continuously follow a specific target described in natural language using only onboard vision. While recent vision-language-action (VLA) policies unify target identification and trajectory planning, their chain-of-thought (CoT) reasoning often operates in abstract spatial latents that are difficult to supervise and weakly aligned with explicit image-space detections. To address this, we introduce ReferTrack, a referring-then-tracking paradigm that grounds EVT using a single forward-facing camera. Our model first selects the target from an indexed set of bounding boxes, then decodes tracking waypoints conditioned on this image-grounded decision. To preserve target motion cues over time, ReferTrack maintains a sliding-window queue of previously selected bounding boxes, injecting their geometric features into the visual history via temporal-viewpoint-bbox indicator (TVBI) tokens. We further enhance target identification by co-training on a custom Refer-QA dataset. On EVT-Bench, ReferTrack achieves state-of-the-art single-view performance with success rates of 89.4%, 73.3%, and 74.1% on the single-target, distracted, and ambiguity tracking splits, respectively – matching or even surpassing several multi-camera baselines on identification-heavy tasks. Finally, real-world deployments on legged and humanoid robots validate its robust sim-to-real transfer capabilities. Code is available at https://github.com/MedlarTea/referTrack.

Hanjing Ye, Tianle Zeng, Jiazhao ZhangJul 22, 2026
VLAObject trackingReferenceJul 22, 2026
CosFly-VLA: A Spatially Aware Vision-Language-Action Model for UAV Tracking

CosFly-VLA: A Spatially Aware Vision-Language-Action Model for UAV Tracking

Dynamic target tracking is essential for Unmanned Aerial Vehicles (UAVs) operating in complex urban environments, where both the target and the camera viewpoint change continuously. Existing Vision-Language-Action (VLA) policies can track visible targets effectively, but their performance often degrades when buildings, vegetation, or roadside objects block the line of sight. During sustained occlusion, a policy may lose the target state, execute actions toward an incorrect region, and amplify this error through subsequent observations until re-acquisition becomes impossible. To this end, we present CosFly-VLA, a spatially aware VLA model that jointly grounds the target, estimates its visibility, and generates continuous flight actions through a structured prediction interface. To train this policy, we use a large-scale recipe over diverse data sources. Spatially Grounded Continued Pretraining (CPT) on a 500k mixed pool injects UAV-view depth, distance, and 3-D spatial reasoning. A three-stage Curriculum-based Supervised Fine-Tuning (SFT) process then specializes the tracker through multi-head warm-up followed by two-stage curriculum learning over natural and hard / long-occlusion data. Chain-of-Thought (CoT) training subsequently teaches recovery-oriented reasoning traces before structured answers. Finally, a closed-loop Reinforcement Learning (RL) stage optimizes tracking behavior with a multi-component reward covering stand-off tracking, grounding quality, collision avoidance, and task success. Relative to OpenVLA, CosFly-VLA-0.8B reduces open-loop Average Displacement Error (ADE) by 34.1% on seen-test and 35.3% on unseen-test. Closed-loop optimization improves Success Rate (SR) by 29.8% and 2.5%, respectively. These results demonstrate progress from visible-frame imitation toward spatially grounded action-closed-loop control, evaluated under a shared oracle state history.

Ruilong Ren, Songsheng Cheng, Yunpeng ZhouJul 16, 2026
UAVVLATrackingJul 16, 2026