Leveraging Event Streams with Deep Reinforcement Learning for End-to-End UAV Tracking

• URL: http://arxiv.org/abs/2410.14685v1
• Date: Thu, 03 Oct 2024 07:56:40 GMT
• Title: Leveraging Event Streams with Deep Reinforcement Learning for End-to-End UAV Tracking
• Authors: Ala Souissi, Hajer Fradi, Panagiotis Papadakis,
• Abstract summary: We present our proposed approach for active tracking to increase the autonomy of Unmanned Aerial Vehicles (UAVs) using event cameras, low-energy imaging sensors. The proposed tracking controller is designed to respond to visual feedback from the mounted event sensor, adjusting the drone movements to follow the target.
• Score: 1.8297494098768172
• License:
• Abstract: In this paper, we present our proposed approach for active tracking to increase the autonomy of Unmanned Aerial Vehicles (UAVs) using event cameras, low-energy imaging sensors that offer significant advantages in speed and dynamic range. The proposed tracking controller is designed to respond to visual feedback from the mounted event sensor, adjusting the drone movements to follow the target. To leverage the full motion capabilities of a quadrotor and the unique properties of event sensors, we propose an end-to-end deep-reinforcement learning (DRL) framework that maps raw sensor data from event streams directly to control actions for the UAV. To learn an optimal policy under highly variable and challenging conditions, we opt for a simulation environment with domain randomization for effective transfer to real-world environments. We demonstrate the effectiveness of our approach through experiments in challenging scenarios, including fast-moving targets and changing lighting conditions, which result in improved generalization capabilities.

Related papers

• E-Motion: Future Motion Simulation via Event Sequence Diffusion [86.80533612211502]
  Event-based sensors may potentially offer a unique opportunity to predict future motion with a level of detail and precision previously unachievable. We propose to integrate the strong learning capacity of the video diffusion model with the rich motion information of an event camera as a motion simulation framework. Our findings suggest a promising direction for future research in enhancing the interpretative power and predictive accuracy of computer vision systems.
  arXiv  Detail & Related papers  (2024-10-11T09:19:23Z)
• Optimal OnTheFly Feedback Control of Event Sensors [0.14999444543328289]
  Event-based vision sensors produce an asynchronous stream of events which are triggered when pixel intensity variation exceeds a threshold. We propose an approach for dynamic feedback control of activation thresholds, in which a controller network analyzes the past emitted events. We demonstrate that our approach outperforms both fixed and randomly-varying threshold schemes by 6-12% in terms of LPIPS perceptual image dissimilarity metric.
  arXiv  Detail & Related papers  (2024-08-23T10:49:16Z)
• EVIT: Event-based Visual-Inertial Tracking in Semi-Dense Maps Using Windowed Nonlinear Optimization [19.915476815328294]
  Event cameras are an interesting visual exteroceptive sensor that reacts to brightness changes rather than integrating absolute image intensities. This paper proposes the addition of inertial signals in order to robustify the estimation. Our evaluation focuses on a diverse set of real world sequences and comprises a comparison of our proposed method against a purely event-based alternative running at different rates.
  arXiv  Detail & Related papers  (2024-08-02T16:24:55Z)
• Motion Segmentation for Neuromorphic Aerial Surveillance [42.04157319642197]
  Event cameras offer superior temporal resolution, superior dynamic range, and minimal power requirements. Unlike traditional frame-based sensors that capture redundant information at fixed intervals, event cameras asynchronously record pixel-level brightness changes. We introduce a novel motion segmentation method that leverages self-supervised vision transformers on both event data and optical flow information.
  arXiv  Detail & Related papers  (2024-05-24T04:36:13Z)
• OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising [49.86409475232849]
  Trajectory prediction is fundamental in computer vision and autonomous driving. Existing approaches in this field often assume precise and complete observational data. We present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique.
  arXiv  Detail & Related papers  (2024-04-02T18:30:29Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• On the Generation of a Synthetic Event-Based Vision Dataset for Navigation and Landing [69.34740063574921]
  This paper presents a methodology for generating event-based vision datasets from optimal landing trajectories. We construct sequences of photorealistic images of the lunar surface with the Planet and Asteroid Natural Scene Generation Utility. We demonstrate that the pipeline can generate realistic event-based representations of surface features by constructing a dataset of 500 trajectories.
  arXiv  Detail & Related papers  (2023-08-01T09:14:20Z)
• Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles with Joint Radar-Data Communications [69.24726496448713]
  We propose an intelligent optimization framework based on the Markov Decision Process (MDP) to help the AV make optimal decisions. We then develop an effective learning algorithm leveraging recent advances of deep reinforcement learning techniques to find the optimal policy for the AV. We show that the proposed transferable deep reinforcement learning framework reduces the obstacle miss detection probability by the AV up to 67% compared to other conventional deep reinforcement learning approaches.
  arXiv  Detail & Related papers  (2021-05-28T08:45:37Z)
• Energy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Space [62.39318039798564]
  We propose a scheduling mechanism capable of taking advantage of correlated information. The proposed mechanism is capable of determining the frequency with which sensors should transmit their updates. We show that our solution can significantly extend the sensors' lifetime.
  arXiv  Detail & Related papers  (2020-11-19T09:53:27Z)
• Vehicle Tracking in Wireless Sensor Networks via Deep Reinforcement Learning [15.252556567830215]
  Decentralized vehicle tracking strategy is conceived for improving both tracking accuracy and energy saving. Two deep reinforcement learning (DRL) aided solutions are proposed relying on the dynamic selection of the activation area radius.
  arXiv  Detail & Related papers  (2020-02-22T10:01:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.