首页/论文/2002.02256

arXiv 2002.022562020-02-06cs.CV

Looking GLAMORous: Vehicle Re-Id in Heterogeneous Cameras Networks with Global and Local Attention

Georgia Institute of Technology（佐治亚理工学院）

Abhijit Suprem, Calton Pu

英文摘要：

Vehicle re-identification (re-id) is a fundamental problem for modern surveillance camera networks. Existing approaches for vehicle re-id utilize global features and local features for re-id by combining multiple subnetworks and losses. In this paper, we propose GLAMOR, or Global and Local Attention MOdules for Re-id. GLAMOR performs global and local feature extraction simultaneously in a unified model to achieve state-of-the-art performance in vehicle re-id across a variety of adversarial conditions and datasets (mAPs 80.34, 76.48, 77.15 on VeRi-776, VRIC, and VeRi-Wild, respectively). GLAMOR introduces several contributions: a better backbone construction method that outperforms recent approaches, group and layer normalization to address conflicting loss targets for re-id, a novel global attention module for global feature extraction, and a novel local attention module for self-guided part-based local feature extraction that does not require supervision. Additionally, GLAMOR is a compact and fast model that is 10x smaller while delivering 25% better performance.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

阅读原文

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

本文内容

英文摘要相关论文

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

继续探索

相关论文

Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification

Norwegian University of Science and Technology（挪威科技大学）；University of Verona（维罗纳大学）；Sapienza Rome University（罗马第一大学）

Re-identification is generally carried out by encoding the appearance of a subject in terms of outfit, suggesting scenarios where people do not change their attire. In this paper we overcome this restriction, by proposing a framework based on a deep convolutional neural network, SOMAnet, that additionally models other discriminative aspects, namely, structural attributes of the human figure (e.g. height, obesity, gender). Our method is unique in many respects. First, SOMAnet is based on the Inception architecture, departing from the usual siamese framework. This spares expensive data preparati…

2018-11-13cs.CV

Human Attention Detection Using AM-FM Representations

University of New Mexico（新墨西哥大学）

Human activity detection from digital videos presents many challenges to the computer vision and image processing communities. Recently, many methods have been developed to detect human activities with varying degree of success. Yet, the general human activity detection problem remains very challenging, especially when the methods need to work 'in the wild' (e.g., without having precise control over the imaging geometry). The thesis explores phase-based solutions for (i) detecting faces, (ii) back of the heads, (iii) joint detection of faces and back of the heads, and (iv) whether the head is …

2022-03-09cs.CVeess.IV

FLSea: Underwater Visual-Inertial and Stereo-Vision Forward-Looking Datasets

University of Haifa（海法大学）

Visibility underwater is challenging, and degrades as the distance between the subject and camera increases, making vision tasks in the forward-looking direction more difficult. We have collected underwater forward-looking stereo-vision and visual-inertial image sets in the Mediterranean and Red Sea. To our knowledge there are no other public datasets in the underwater environment acquired with this camera-sensor orientation published with ground-truth. These datasets are critical for the development of several underwater applications, including obstacle avoidance, visual odometry, 3D tracking…

2023-02-24cs.CVcs.RO

PCIE_LAM 方案用于 Ego4D Looking At Me 挑战赛

PCIE_LAM Solution for Ego4D Looking At Me Challenge

Lenovo Research（联想研究院）；Chinese Academy of Sciences（中国科学院）；Beijing Jiaotong University（北京交通大学）

本研究针对 Ego4D Looking At Me 挑战赛，提出结合 InternVL 与 Bi-LSTM 的 InternLSTM 方案，辅以视线平滑滤波器处理模糊噪声，最终以 0.81 mAP、0.93 准确率夺冠。

2024-06-18cs.CV

Tracking Emerges by Looking Around Static Scenes, with Neural 3D Mapping

Carnegie Mellon University（卡内基梅隆大学）

We hypothesize that an agent that can look around in static scenes can learn rich visual representations applicable to 3D object tracking in complex dynamic scenes. We are motivated in this pursuit by the fact that the physical world itself is mostly static, and multiview correspondence labels are relatively cheap to collect in static scenes, e.g., by triangulation. We propose to leverage multiview data of \textit{static points} in arbitrary scenes (static or dynamic), to learn a neural 3D mapping module which produces features that are correspondable across time. The neural 3D mapper consumes…

2020-08-04cs.CV

由内而外：从视频中预测驾驶员意图

Looking Inside Out: Anticipating Driver Intent From Videos

University of Texas at Austin（德克萨斯大学奥斯汀分校）

本文提出联合利用舱内与外部摄像头数据并显式提取物体级和道路级特征，结合Transformer和LSTM模型，实现更早且更准确的驾驶员意图预测，准确率达87.5%，平均提前4.35秒。

2023-12-03cs.CVcs.HC

更多相关论文收起更多论文

Real Time Incremental Image Mosaicking Without Use of Any Camera Parameter

Huawei（华为）

Over the past decade, there has been a significant increase in the use of Unmanned Aerial Vehicles (UAVs) to support a wide variety of missions, such as remote surveillance, vehicle tracking, and object detection. For problems involving processing of areas larger than a single image, the mosaicking of UAV imagery is a necessary step. Real-time image mosaicking is used for missions that requires fast response like search and rescue missions. It typically requires information from additional sensors, such as Global Position System (GPS) and Inertial Measurement Unit (IMU), to facilitate direct o…

2022-12-05cs.CV

Topology Preserving Local Road Network Estimation from Single Onboard Camera Image

ETH Zurich（苏黎世联邦理工学院）；KU Leuven（鲁汶大学）

Knowledge of the road network topology is crucial for autonomous planning and navigation. Yet, recovering such topology from a single image has only been explored in part. Furthermore, it needs to refer to the ground plane, where also the driving actions are taken. This paper aims at extracting the local road network topology, directly in the bird's-eye-view (BEV), all in a complex urban setting. The only input consists of a single onboard, forward looking camera image. We represent the road topology using a set of directed lane curves and their interactions, which are captured using their int…

2022-03-30cs.CV

Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching

Australian National University（澳大利亚国立大学）；Australian Centre for Robotic Vision（澳大利亚机器人视觉中心）；University of Technology Sydney（悉尼科技大学）

Cross-view geo-localization is the problem of estimating the position and orientation (latitude, longitude and azimuth angle) of a camera at ground level given a large-scale database of geo-tagged aerial (e.g., satellite) images. Existing approaches treat the task as a pure location estimation problem by learning discriminative feature descriptors, but neglect orientation alignment. It is well-recognized that knowing the orientation between ground and aerial images can significantly reduce matching ambiguity between these two views, especially when the ground-level images have a limited Field …

2020-05-08cs.CV

Are you really looking at me? A Feature-Extraction Framework for Estimating Interpersonal Eye Gaze from Conventional Video

University of Rochester（罗切斯特大学）

Despite a revolution in the pervasiveness of video cameras in our daily lives, one of the most meaningful forms of nonverbal affective communication, interpersonal eye gaze, i.e. eye gaze relative to a conversation partner, is not available from common video. We introduce the Interpersonal-Calibrating Eye-gaze Encoder (ICE), which automatically extracts interpersonal gaze from video recordings without specialized hardware and without prior knowledge of participant locations. Leveraging the intuition that individuals spend a large portion of a conversation looking at each other enables the ICE …

2020-01-17cs.CVcs.HC

Assessment of Deep Convolutional Neural Networks for Road Surface Classification

When parameterizing vehicle control algorithms for stability or trajectory control, the road-tire friction coefficient is an essential model parameter when it comes to control performance. One major impact on the friction coefficient is the condition of the road surface. A camera-based, forward-looking classification of the road-surface helps enabling an early parametrization of vehicle control algorithms. In this paper, we train and compare two different Deep Convolutional Neural Network models, regarding their application for road friction estimation and describe the challenges for training …

2018-08-07cs.CV

Eye Contact Correction using Deep Neural Networks

Intel Corporation（英特尔公司）

In a typical video conferencing setup, it is hard to maintain eye contact during a call since it requires looking into the camera rather than the display. We propose an eye contact correction model that restores the eye contact regardless of the relative position of the camera and display. Unlike previous solutions, our model redirects the gaze from an arbitrary direction to the center without requiring a redirection angle or camera/display/user geometry as inputs. We use a deep convolutional neural network that inputs a monocular image and produces a vector field and a brightness map to corre…

2019-12-26cs.CVcs.MM

Single-Image Depth Prediction Makes Feature Matching Easier

Chalmers University of Technology（查尔姆斯理工大学）；Niantic；University College London（伦敦大学学院）

Good local features improve the robustness of many 3D re-localization and multi-view reconstruction pipelines. The problem is that viewing angle and distance severely impact the recognizability of a local feature. Attempts to improve appearance invariance by choosing better local feature points or by leveraging outside information, have come with pre-requisites that made some of them impractical. In this paper, we propose a surprisingly effective enhancement to local feature extraction, which improves matching. We show that CNN-based depths inferred from single RGB images are quite helpful, de…

2020-08-21cs.CV

Deep 3D Pan via adaptive "t-shaped" convolutions with global and local adaptive dilations

Korea Advanced Institute of Science and Technology（韩国高等科学技术院）

Recent advances in deep learning have shown promising results in many low-level vision tasks. However, solving the single-image-based view synthesis is still an open problem. In particular, the generation of new images at parallel camera views given a single input image is of great interest, as it enables 3D visualization of the 2D input scenery. We propose a novel network architecture to perform stereoscopic view synthesis at arbitrary camera positions along the X-axis, or Deep 3D Pan, with "t-shaped" adaptive kernels equipped with globally and locally adaptive dilations. Our proposed network…

2019-10-21cs.CVeess.IVeess.SP

TPE-Net：用于轨道路径生成的轨点提取与关联网络

TPE-Net: Track Point Extraction and Association Network for Rail Path Proposal Generation

Thales Group（泰雷兹集团）；York University（约克大学）；Lassonde School of Engineering（拉桑德工程学院）

提出TPE-Net全卷积网络，通过回归中心点及左右轨道像素并聚类生成路线拓扑，在无3D数据与几何约束下高效提取本车路径，实现0.9207精度与0.8721召回率。

2023-02-11cs.AIcs.CV