← Back to all news
arXiv Computer Vision By Oliver Bimber, Rakesh John Amala Arokia Nathan, Mohamed Youssef, Vinayak Lal Bhatnagar, Ralf Berger, Klaus Hackl\"ander

trACT: temporal revelation Airborne Camera Trap

Read the original on arXiv Computer Vision →

trACT (temporal revelation Airborne Camera Trap) is a lightweight, real‑time aerial robotics framework for autonomous consumer drones. It uses Temporal Max Pooling to convert subtle ground motion into robust signals, coupled with self‑supervised motion anomaly detection to separate target movement from environmental noise. The system also incorporates motion prediction, gimbal‑stabilized optical zoom verification, and balanced multi‑target verification, enabling precise target confirmation in challenging forested and surveillance environments.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv AI
Sep 4

Decentralized Vision-Based Autonomous Aerial Wildlife Monitoring

The paper introduces a decentralized, vision-based system using multiple quadrotors equipped with a single RGB camera for monitoring wildlife. It emphasizes scalability, low bandwidth, and minimal sensor requirements, enabling robust identification and tracking of large species in natural habitats. The authors present novel coordination and tracking algorithms that operate without centralized communication, and validate the approach with real-world field experiments.

By Makram Chahine, William Yang, Alaa Maalouf, Justin Siriska, Ninad Jadhav, Daniel Vogt, Stephanie Gil, Robert Wood, Daniela Rus
agentsroboticssafety
More like this →
arXiv Machine Learning
Jun 3

PerchRL: Vision-Based Agile Perching on Inclined Platforms under Rapid and Irregular Motion

arXiv:2606. 03441v1 Announce Type: cross Abstract: Autonomous vision-based perching of quadrotors on moving inclined platforms is critical for air-ground collaboration but remains challenging due to the limited field of view (FOV).

By Zihong Lu, Zongzhuo Liu, Huaxu Li, Jinqiang Cui, Jie Mei, Youmin Gong, U Kei Cheang, Boyu Zhou
agentsreinforcement-learningfine-tuning
More like this →
arXiv Computer Vision
Sep 21

Field Tracking of Insects Using a Stereoscopic Event-Based Camera Setup

The paper presents a method for tracking insects in the field using a stereoscopic event-based camera setup. By converting asynchronous events into conventional video formats, the authors combine the high temporal resolution of event cameras with standard video processing techniques to capture detailed insect flight movements. The stereoscopic configuration enables continuous, low‑latency 3D tracking, reducing motion blur and improving accuracy in natural environments.

By Pratham G. Shenwai, Martin J. Lankheet, John T. Hrynuk, Mandiyam Y. Mahadeeswara, Mandyam V. Srinivasan, Sridhar Ravi
More like this →