Gaining Scale Invariance in UAV Bird's Eye View Object Detection by Adaptive Resizing

• URL: http://arxiv.org/abs/2101.12694v1
• Date: Fri, 29 Jan 2021 17:26:38 GMT
• Title: Gaining Scale Invariance in UAV Bird's Eye View Object Detection by Adaptive Resizing
• Authors: Martin Messmer, Benjamin Kiefer, Andreas Zell
• Abstract summary: We introduce a new preprocessing step applicable to UAV bird's eye view imagery, which we call Adaptive Resizing. It is constructed to adjust the vast variances in objects' scales, which are naturally inherent to UAV data sets. We test this extensively on UAVDT, VisDrone, and on a new data set, we captured ourselves.
• Score: 14.853897011640022
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we introduce a new preprocessing step applicable to UAV bird's eye view imagery, which we call Adaptive Resizing. It is constructed to adjust the vast variances in objects' scales, which are naturally inherent to UAV data sets. Furthermore, it improves inference speed by four to five times on average. We test this extensively on UAVDT, VisDrone, and on a new data set, we captured ourselves. On UAVDT, we achieve more than 100 % relative improvement in AP50. Moreover, we show how this method can be applied to a general UAV object detection task. Additionally, we successfully test our method on a domain transfer task where we train on some interval of altitudes and test on a different one. Code will be made available at our website.

Related papers

• Rotation Invariant Transformer for Recognizing Object in UAVs [66.1564328237299]
  We propose a novel rotation invariant vision transformer (RotTrans) forRecognizing targets of interest from UAVs. RotTrans greatly outperforms the current state-of-the-arts, which is 5.9% and 4.8% higher than the highest mAP and Rank1. Our solution wins the first place in the UAV-based person re-recognition track in the Multi-Modal Video Reasoning and Analyzing Competition.
  arXiv  Detail & Related papers  (2023-11-05T03:55:08Z)
• Evidential Detection and Tracking Collaboration: New Problem, Benchmark and Algorithm for Robust Anti-UAV System [56.51247807483176]
  Unmanned Aerial Vehicles (UAVs) have been widely used in many areas, including transportation, surveillance, and military. Previous works have simplified such an anti-UAV task as a tracking problem, where prior information of UAVs is always provided. In this paper, we first formulate a new and practical anti-UAV problem featuring the UAVs perception in complex scenes without prior UAVs information.
  arXiv  Detail & Related papers  (2023-06-27T19:30:23Z)
• Revisiting Stereo Triangulation in UAV Distance Estimation [5.656973345209692]
  We build and present a UAVDE dataset for UAV distance estimation, in which distance between two UAVs is obtained by UWB sensors. We propose a novel position correction module, which can directly predict the offset between the observed positions and the actual ones. We conduct extensive experiments on UAVDE, and our method can achieve a significant performance improvement over a strong baseline.
  arXiv  Detail & Related papers  (2023-06-15T08:22:05Z)
• UAV Obstacle Avoidance by Human-in-the-Loop Reinforcement in Arbitrary 3D Environment [17.531224704021273]
  This paper focuses on the continuous control of the unmanned aerial vehicle (UAV) based on a deep reinforcement learning method. We propose a deep reinforcement learning (DRL)-based method combined with human-in-the-loop, which allows the UAV to avoid obstacles automatically during flying.
  arXiv  Detail & Related papers  (2023-04-07T01:44:05Z)
• AZTR: Aerial Video Action Recognition with Auto Zoom and Temporal Reasoning [63.628195002143734]
  We propose a novel approach for aerial video action recognition. Our method is designed for videos captured using UAVs and can run on edge or mobile devices. We present a learning-based approach that uses customized auto zoom to automatically identify the human target and scale it appropriately.
  arXiv  Detail & Related papers  (2023-03-02T21:24:19Z)
• A$^2$-UAV: Application-Aware Content and Network Optimization of Edge-Assisted UAV Systems [12.847650904294033]
  We propose a novel A$2$-UAV framework to optimize the number of correctly executed tasks at the edge. A$2$-TPP takes into account the relationship between deep neural network (DNN) accuracy and image compression. We extensively evaluate A$2$-TPP through real-world experiments with a testbed composed by four DJI Mavic Air 2 UAVs.
  arXiv  Detail & Related papers  (2023-01-16T11:17:32Z)
• Vision-Based UAV Self-Positioning in Low-Altitude Urban Environments [20.69412701553767]
  Unmanned Aerial Vehicles (UAVs) rely on satellite systems for stable positioning. In such situations, vision-based techniques can serve as an alternative, ensuring the self-positioning capability of UAVs. This paper presents a new dataset, DenseUAV, which is the first publicly available dataset designed for the UAV self-positioning task.
  arXiv  Detail & Related papers  (2022-01-23T07:18:55Z)
• Rethinking Drone-Based Search and Rescue with Aerial Person Detection [79.76669658740902]
  The visual inspection of aerial drone footage is an integral part of land search and rescue (SAR) operations today. We propose a novel deep learning algorithm to automate this aerial person detection (APD) task. We present the novel Aerial Inspection RetinaNet (AIR) algorithm as the combination of these contributions.
  arXiv  Detail & Related papers  (2021-11-17T21:48:31Z)
• Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking [59.06167734555191]
  Unmanned Aerial Vehicle (UAV) offers lots of applications in both commerce and recreation. We consider the task of tracking UAVs, providing rich information such as location and trajectory. We propose a dataset, Anti-UAV, with more than 300 video pairs containing over 580k manually annotated bounding boxes.
  arXiv  Detail & Related papers  (2021-01-21T07:00:15Z)
• Perceiving Traffic from Aerial Images [86.994032967469]
  We propose an object detection method called Butterfly Detector that is tailored to detect objects in aerial images. We evaluate our Butterfly Detector on two publicly available UAV datasets (UAVDT and VisDrone 2019) and show that it outperforms previous state-of-the-art methods while remaining real-time.
  arXiv  Detail & Related papers  (2020-09-16T11:37:43Z)

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.