A Novel Dual-pooling Attention Module for UAV Vehicle Re-identification

• URL: http://arxiv.org/abs/2306.14104v1
• Date: Sun, 25 Jun 2023 02:46:12 GMT
• Title: A Novel Dual-pooling Attention Module for UAV Vehicle Re-identification
• Authors: Xiaoyan Guo, Jie Yang, Xinyu Jia, Chuanyan Zang, Yan Xu, Zhaoyang Chen
• Abstract summary: Vehicle re-identification (Re-ID) involves identifying the same vehicle captured by other cameras, given a vehicle image. Due to the high altitude of UAVs, the shooting angle of vehicle images sometimes approximates vertical, resulting in fewer local features for Re-ID. This paper proposes a novel dual-pooling attention (DpA) module, which achieves the extraction and enhancement of locally important information about vehicles.
• Score: 7.9782462757515455
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vehicle re-identification (Re-ID) involves identifying the same vehicle captured by other cameras, given a vehicle image. It plays a crucial role in the development of safe cities and smart cities. With the rapid growth and implementation of unmanned aerial vehicles (UAVs) technology, vehicle Re-ID in UAV aerial photography scenes has garnered significant attention from researchers. However, due to the high altitude of UAVs, the shooting angle of vehicle images sometimes approximates vertical, resulting in fewer local features for Re-ID. Therefore, this paper proposes a novel dual-pooling attention (DpA) module, which achieves the extraction and enhancement of locally important information about vehicles from both channel and spatial dimensions by constructing two branches of channel-pooling attention (CpA) and spatial-pooling attention (SpA), and employing multiple pooling operations to enhance the attention to fine-grained information of vehicles. Specifically, the CpA module operates between the channels of the feature map and splices features by combining four pooling operations so that vehicle regions containing discriminative information are given greater attention. The SpA module uses the same pooling operations strategy to identify discriminative representations and merge vehicle features in image regions in a weighted manner. The feature information of both dimensions is finally fused and trained jointly using label smoothing cross-entropy loss and hard mining triplet loss, thus solving the problem of missing detail information due to the high height of UAV shots. The proposed method's effectiveness is demonstrated through extensive experiments on the UAV-based vehicle datasets VeRi-UAV and VRU.

Related papers

• Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics [77.34726150561087]
  This work aims to carry out a study on the current scenario of camera and radar-based perception for ADAS and autonomous vehicles. Concepts and characteristics related to both sensors, as well as to their fusion, are presented. We give an overview of the Deep Learning-based detection and segmentation tasks, and the main datasets, metrics, challenges, and open questions in vehicle perception.
  arXiv  Detail & Related papers  (2023-03-08T00:48:32Z)
• SA-NET.v2: Real-time vehicle detection from oblique UAV images with use of uncertainty estimation in deep meta-learning [0.0]
  In this manuscript, we consider the problem of real-time vehicle detection for oblique UAV images based on a small training dataset and deep meta-learning. The SA-Net.v2 is a developed method based on the SA-CNN for real-time vehicle detection by reformulating the squeeze-and-attention mechanism. Experiments show that the SA-Net.v2 achieves promising performance in time series oblique UAV images.
  arXiv  Detail & Related papers  (2022-08-04T09:08:47Z)
• Discriminative-Region Attention and Orthogonal-View Generation Model for Vehicle Re-Identification [7.5366501970852955]
  Multiple challenges hamper the applications of vision-based vehicle Re-ID methods. The proposed DRA model can automatically extract the discriminative region features, which can distinguish similar vehicles. And the OVG model can generate multi-view features based on the input view features to reduce the impact of viewpoint mismatches.
  arXiv  Detail & Related papers  (2022-04-28T07:46:03Z)
• Self-aligned Spatial Feature Extraction Network for UAV Vehicle Re-identification [3.449626476434765]
  Vehicles with same color and type show extremely similar appearance from the UAV's perspective. Recent works tend to extract distinguishing information by regional features and component features. In order to extract efficient fine-grained features and avoid tedious annotating work, this letter develops an unsupervised self-aligned network.
  arXiv  Detail & Related papers  (2022-01-08T14:25:54Z)
• UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-identification [21.48667873335246]
  Recent development in deep learning allows vision-based counter-UAV systems to detect and track UAVs with a single camera. The coverage of a single camera is limited, necessitating the need for multicamera configurations to match UAVs across cameras. We propose the first new UAV re-identification data set, UAV-reID, that facilitates the development of machine learning solutions in this emerging area.
  arXiv  Detail & Related papers  (2021-04-13T14:13:09Z)
• Fine-Grained Vehicle Perception via 3D Part-Guided Visual Data Augmentation [77.60050239225086]
  We propose an effective training data generation process by fitting a 3D car model with dynamic parts to vehicles in real images. Our approach is fully automatic without any human interaction. We present a multi-task network for VUS parsing and a multi-stream network for VHI parsing.
  arXiv  Detail & Related papers  (2020-12-15T03:03:38Z)
• Discriminative Feature Representation with Spatio-temporal Cues for Vehicle Re-identification [0.0]
  Vehicle-identification (re-ID) aims to discover and match the target vehicles from a gallery image set taken by different cameras on a wide range of road networks. We propose a feature representation with novel clues (DFR-ST) for vehicle re-ID. It is capable of building robust features in the embedding space by involving appearance and retemporal information.
  arXiv  Detail & Related papers  (2020-11-13T10:50:21Z)
• 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)
• The Devil is in the Details: Self-Supervised Attention for Vehicle Re-Identification [75.3310894042132]
  Self-supervised Attention for Vehicle Re-identification (SAVER) is a novel approach to effectively learn vehicle-specific discriminative features. We show that SAVER improves upon the state-of-the-art on challenging VeRi, VehicleID, Vehicle-1M and VERI-Wild datasets.
  arXiv  Detail & Related papers  (2020-04-14T02:24:47Z)
• Parsing-based View-aware Embedding Network for Vehicle Re-Identification [138.11983486734576]
  We propose a parsing-based view-aware embedding network (PVEN) to achieve the view-aware feature alignment and enhancement for vehicle ReID. The experiments conducted on three datasets show that our model outperforms state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2020-04-10T13:06:09Z)
• Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning [59.19469551774703]
  Drone-based vehicle detection aims at finding the vehicle locations and categories in an aerial image. We construct a large-scale drone-based RGB-Infrared vehicle detection dataset, termed DroneVehicle. Our DroneVehicle collects 28, 439 RGB-Infrared image pairs, covering urban roads, residential areas, parking lots, and other scenarios from day to night.
  arXiv  Detail & Related papers  (2020-03-05T05:29:44Z)

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.