Vehicle Attribute Recognition by Appearance: Computer Vision Methods for Vehicle Type, Make and Model Classification

• URL: http://arxiv.org/abs/2006.16400v1
• Date: Mon, 29 Jun 2020 21:33:06 GMT
• Title: Vehicle Attribute Recognition by Appearance: Computer Vision Methods for Vehicle Type, Make and Model Classification
• Authors: Xingyang Ni, Heikki Huttunen
• Abstract summary: We survey a number of algorithms that identify vehicle properties ranging from coarse-grained level (vehicle type) to fine-grained level (vehicle make and model) We discuss two alternative approaches for these tasks, including straightforward classification and a more flexible metric learning method. We design a simulated real-world scenario for vehicle attribute recognition and present an experimental comparison of the two approaches.
• Score: 0.9645196221785693
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper studies vehicle attribute recognition by appearance. In the literature, image-based target recognition has been extensively investigated in many use cases, such as facial recognition, but less so in the field of vehicle attribute recognition. We survey a number of algorithms that identify vehicle properties ranging from coarse-grained level (vehicle type) to fine-grained level (vehicle make and model). Moreover, we discuss two alternative approaches for these tasks, including straightforward classification and a more flexible metric learning method. Furthermore, we design a simulated real-world scenario for vehicle attribute recognition and present an experimental comparison of the two approaches.

Related papers

• A Feature-based Approach for the Recognition of Image Quality Degradation in Automotive Applications [0.0]
  This paper presents a feature-based algorithm to detect certain effects that can degrade image quality in automotive applications. Experiments with different data sets show that the algorithm can detect soiling adhering to camera lenses and classify different types of image degradation.
  arXiv  Detail & Related papers  (2023-03-13T13:40:09Z)
• Image-Based Vehicle Classification by Synergizing Features from Supervised and Self-Supervised Learning Paradigms [0.913755431537592]
  Two state-of-the-art self-supervised learning methods, DINO and data2vec, were evaluated and compared for their representation learning of vehicle images. The representations learned from these self-supervised learning methods were combined with the wheel positional features for the vehicle classification task. Our experiments show that the data2vec-distilled representations, which are consistent with our wheel masking strategy, outperformed the DINO counterpart.
  arXiv  Detail & Related papers  (2023-02-01T18:22:23Z)
• Deep Learning Computer Vision Algorithms for Real-time UAVs On-board Camera Image Processing [77.34726150561087]
  This paper describes how advanced deep learning based computer vision algorithms are applied to enable real-time on-board sensor processing for small UAVs. All algorithms have been developed using state-of-the-art image processing methods based on deep neural networks.
  arXiv  Detail & Related papers  (2022-11-02T11:10:42Z)
• Towards Intrinsic Common Discriminative Features Learning for Face Forgery Detection using Adversarial Learning [59.548960057358435]
  We propose a novel method which utilizes adversarial learning to eliminate the negative effect of different forgery methods and facial identities. Our face forgery detection model learns to extract common discriminative features through eliminating the effect of forgery methods and facial identities.
  arXiv  Detail & Related papers  (2022-07-08T09:23:59Z)
• 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)
• Automated Approach for Computer Vision-based Vehicle Movement Classification at Traffic Intersections [7.3496760394236595]
  We propose an automated classification method for movement specific classification of vision-based vehicle trajectories. Our framework identifies different movement patterns observed in a traffic scene using an unsupervised hierarchical clustering technique. A new similarity measure was designed to overcome the inherent shortcomings of vision-based trajectories.
  arXiv  Detail & Related papers  (2021-11-17T15:02:43Z)
• Recent Progress in Appearance-based Action Recognition [73.6405863243707]
  Action recognition is a task to identify various human actions in a video. Recent appearance-based methods have achieved promising progress towards accurate action recognition.
  arXiv  Detail & Related papers  (2020-11-25T10:18:12Z)
• PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data [34.66187690224724]
  Vehicle ReID is challenging due to 1) high intra-class variability (caused by the dependency of shape and appearance on viewpoint), and 2) small inter-class variability (caused by the similarity in shape and appearance between vehicles produced by different manufacturers) We propose a Pose-Aware Multi-Task Re-Identification (PAMTRI) framework. It overcomes viewpoint-dependency by explicitly reasoning about vehicle pose and shape via keypoints, heatmaps and segments from pose estimation. It jointly classifies semantic vehicle attributes (colors and types) while performing ReID, through multi-task learning with the embedded pose representations.
  arXiv  Detail & Related papers  (2020-05-02T01:29:09Z)
• VehicleNet: Learning Robust Visual Representation for Vehicle Re-identification [116.1587709521173]
  We propose to build a large-scale vehicle dataset (called VehicleNet) by harnessing four public vehicle datasets. We design a simple yet effective two-stage progressive approach to learning more robust visual representation from VehicleNet. We achieve the state-of-art accuracy of 86.07% mAP on the private test set of AICity Challenge.
  arXiv  Detail & Related papers  (2020-04-14T05:06:38Z)
• 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)

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.