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Deep Reinforcement Learning-based Tracker and
Il y a 4 mois
**Deep Reinforcement Learning-based Tracker and Control for Drones**:
- Réf
- **ABG-117647**
- Stage master 2 / Ingénieur- Durée 4 mois- Salaire net mensuel 4.05 (l'heure)- 07/11/2023- IMT Atlantique- Lieu de travail- Plouzané Bretagne France- Champs scientifiques- Informatique
- Mots clés- computer vision, robotics, drones, control, deep reinforcement learning, event sensors**Établissement recruteur**:
RAMBO team, Lab-STICC, IMT Atlantique, Brest (France)
**Description**:
Context:
This project will be conducted in the context of LEASARD project [1] which aims to increase the navigation autonomy of drones as Unmanned Aerial Vehicles (AUV) in search and rescue scenarios. Towards this goal, LEASARD project is dedicated to the enhancement of sensing and processing capabilities through the integration of event cameras and the utilization of appropriate deep neural networks to process data from these cameras.
Description and objectives:
This project aims to propose a deep reinforcement learning (RL) based visual tracker for drones (aerial) images. Our primary motivation is to use deep reinforcement learning in order to identify a control policy, thereby enhancing the performance of long-term tracking.
Given the challenges associated with object tracking in aerial images [2,3], particularly due to the small size of the target objects, variations in scale, orientation, and viewpoints, as well as the movement of both the drone's camera and the target, event cameras (also known as Dynamic Vision Sensors (DVS)) [4], are proposed to substitute conventional RGB cameras. These cameras offer unique properties, including low latency, free from to motion blur, lower energy requirements, and a high dynamic range, making them more suitable for processing drone videos.
References:
[2] Li, S., & Yeung, D. Y. (2017, February). Visual object tracking for unmanned aerial vehicles: A benchmark and new motion models. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 31, No. 1).
[3] Hamdi, A., Salim, F., & Kim, D. Y. (2020, July). Drotrack: High speed drone-based object tracking under uncertainty. In 2020 IEEE international conference on fuzzy systems (FUZZ-IEEE) (pp. 1-8). IEEE.
[4] Gallego, G., Delbrück, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., & Scaramuzza, D. (2020). Event-based vision: A survey. IEEE transactions on pattern analysis and machine intelligence, 44(1), 154-180.
[5] Ma, B., Liu, Z., Zhao, W., Yuan, J., Long, H., Wang, X., & Yuan, Z. (2023). Target Tracking Control of UAV Through Deep Reinforcement Learning. IEEE Transactions on Intelligent Transportation Systems.
[6] Zhu, P., Wen, L., Du, D., Bian, X., Fan, H., Hu, Q., & Ling, H. (2021). Detection and tracking meet drones challenge. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11), 7380-7399.
**Profil**:
- A strong level of Python programming is required.
- An interest in deep learning frameworks and mainly in deep reinforcement learning is also required.
- Experience with ROS would be a plus. - Good oral and written communication skills in English
**Prise de fonction**:
- Dès que possible
