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An Improved Association Pipeline for Multi-Person Tracking

An Improved Association Pipeline for Multi-Person Tracking

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Stadler, Daniel Beyerer, Jürgen Karlsruhe Institute of Technology Germany Fraunhofer IOSB Germany Fraunhofer Center for Machine Learning Germany

ISBN: (纸本)9798350302493

The association task of assigning detections to tracks in multi-person tracking has recently been improved by integration of a second matching stage for low-confident detections that are usually discarded in the tracking process. Despite its success, we find that this two stage matching has some weaknesses. For example, high-confident detections are preferred over low-confident detections in any case, even if the low-confident ones are more accurate. Therefore, a Combined Matching (CM) is proposed which considers all possible assignments simultaneously in a single matching stage and thus improves the association accuracy. Moreover, shortcomings of existing motion and appearance distance combinations are identified and a novel Combined Distance (CD) for motion and appearance information is introduced that significantly outperforms previous fusion approaches. Furthermore, we propose an Occlusion Aware Initialization (OAI) which prevents the start of ghost tracks from duplicate detections under occlusion. The effectiveness of our components is shown with extensive ablative experiments and the competitiveness of our tracker is demonstrated on the MOT17 and MOT20 benchmarks, where the current state-of-the-art is notably surpassed. © 2023 ieee.

关键词： computer vision

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Natural Language-Based Vehicle Retrieval with Explicit Cross-Modal Representation Learning

Natural Language-Based Vehicle Retrieval with Explicit Cross...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Xu, Bocheng Xiong, Yihua Zhang, Rui Feng, Yanyi Wu, Haifeng Terminus Technol Dept AI R&D Beijing Peoples R China Chongqing Univ Posts & Telecommun Chongqing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

On the account of the explosive growth in the large-scale transportation videos, vehicle retrieval plays an important role in the public transportation security and the intelligent transport system recently. Most vehicle retrieval algorithms are vision-based and consist of vehicle re-identification and vehicle tracking. However, the performance of vision-based vehicle retrieval algorithms is constrained as the limited information provided by traffic video streams. In this paper, we propose a contrastive cross-modal vehicle retrieval solution, maximizing the value of the complementation between natural language representation and vision representation. The framework of the proposed solution includes: (1) Preprocess a source video in four ways for generating local motional semantics and global motional semantics;(2) Correspondingly, preprocess relevant description sentences in two ways, including Textual Local Instance Semantics Extraction (TLISE) and Textual Local Motional Semantics Extraction (TLMSE);(3) Use a two-stream architecture model with four visual encoders and four text encoders to extract visual features and textual embeddings;(4) Fuse visual features and textual embeddings respectively by concatenating them along the feature channel in the order of importance, and use them for retrieval. By using the proposed solution, we achieved MRR score of 33.20%, ranking the 7th place in the AI City Challenge 2022 Track 2.

关键词： Representation learning Visualization Semantics Urban areas computer architecture Feature extraction Robustness

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Embedding Arithmetic of Multimodal Queries for Image Retrieval

Embedding Arithmetic of Multimodal Queries for Image Retriev...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Couairon, Guillaume Douze, Matthijs Cord, Matthieu Schwenk, Holger Meta AI Menlo Pk CA 94025 USA Sorbonne Univ Paris France Valeo St Ouen Sur Seine France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Latent text representations exhibit geometric regularities, such as the famous analogy: queen is to king what woman is to man. Such structured semantic relations were not demonstrated on image representations. Recent works aiming at bridging this semantic gap embed images and text into a multimodal space, enabling the transfer of text-defined transformations to the image modality. We introduce the SIMAT dataset to evaluate the task of Image Retrieval with Multimodal queries. SIMAT contains 6k images and 18k textual transformation queries that aim at either replacing scene elements or changing pairwise relationships between scene elements. The goal is to retrieve an image consistent with the (source image, text transformation) query. We use an image/text matching oracle (OSCAR) to assess whether the image transformation is successful. The SIMAT dataset will be publicly available. We use SIMAT to evaluate the geometric properties of multimodal embedding spaces trained with an image/text matching objective, like CLIP. We show that vanilla CLIP embeddings are not very well suited to transform images with delta vectors, but that a simple finetuning on the COCO dataset can bring dramatic improvements. We also study whether it is beneficial to leverage pretrained universal sentence encoders (FastText, LASER and LaBSE).

关键词： computer vision conferences Semantics Image retrieval Lasers Transforms Image representation

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Multi-Head Distillation for Continual Unsupervised Domain Adaptation in Semantic Segmentation

Multi-Head Distillation for Continual Unsupervised Domain Ad...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Saporta, Antoine Douillard, Arthur Vu, Tuan-Hung Perez, Patrick Cord, Matthieu Sorbonne Univ Paris France valeo ai Paris France Heuritech Paris France

ISBN: (纸本)9781665487399

Unsupervised Domain Adaptation (UDA) is a transfer learning task which aims at training on an unlabeled target domain by leveraging a labeled source domain. Beyond the traditional scope of UDA with a single source domain and a single target domain, real-world perception systems face a variety of scenarios to handle, from varying lighting conditions to many cities around the world. In this context, UDAs with several domains increase the challenges with the addition of distribution shifts within the different target domains. This work focuses on a novel framework for learning UDA, continuous UDA, in which models operate on multiple target domains discovered sequentially, without access to previous target domains. We propose MuHDi, for Multi-Head Distillation, a method that solves the catastrophic forgetting problem, inherent in continual learning tasks. MuHDi performs distillation at multiple levels from the previous model as well as an auxiliary target-specialist segmentation head. We report both extensive ablation and experiments on challenging multi-target UDA semantic segmentation benchmarks to validate the proposed learning scheme and architecture.

关键词： Training Adaptation models Urban areas Semantics Transfer learning computer architecture Benchmark testing

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Analysis of Temporal Tensor Datasets on Product Grassmann Manifold

Analysis of Temporal Tensor Datasets on Product Grassmann Ma...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Batalo, Bojan Souza, Lincon S. Gatto, Bernardo B. Sogi, Naoya Fukui, Kazuhiro Univ Tsukuba Tsukuba Ibaraki Japan AIST Tokyo Japan AIST Tsukuba Ibaraki Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Growing abundance of multi-dimensional data creates a need for efficient data exploration and analysis. In this paper, we address this need by tackling the task of tensor dataset visualization and clustering, as tensors are a natural form of multi-dimensional data. Previous work has shown that representing individual tensor modes via respective linear subspaces and unifying them on the product Grassmann manifold (PGM) is an effective and memory-efficient way of representation. However, such representation may lead to loss of valuable temporal information. To address this issue, we model temporal tensor modes with a Hankel-like matrix, preserving sequence information and encoding it with a linear subspace, fully compatible with PGM. Unifying regular tensor modes and Hankel-like representation of regular tensor modes then enriches representation on the PGM, with minimal increase in computational complexity. By relying on geodesic distance on the manifold, we facilitate analysis of multi-dimensional datasets in two ways: 1) by enabling straightforward visualizations using algorithms such as t-SNE;and 2) by fostering clustering of data using distance- or similarity-based methods such as spectral clustering. We evaluate our approach on hand gesture and action recognition datasets as exemplars of temporal tensor datasets.

关键词： Manifolds Measurement Geometry Tensors Data visualization pattern recognition Gyroscopes

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deepPIC: Deep Perceptual Image Clustering For Identifying Bias In vision Datasets

deepPIC: Deep Perceptual Image Clustering For Identifying Bi...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Jaipuria, Nikita Stevo, Katherine Zhang, Xianling Gaopande, Meghana L. Garcia, Ian Calle Jain, Jinesh Murali, Vidya N. Ford Greenfield Labs Palo Alto CA 94304 USA Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Dataset bias in manually collected datasets is a known problem in computer vision. In safety-critical applications such as autonomous driving, these biases can lead to catastrophic errors from models trained on such datasets, jeopardizing the safety of users and their surroundings. Being able to unpuzzle the bias in a given dataset, and across datasets, is an essential tool for building safe and responsible AI. In this paper, we present deepPIC: deep Perceptual Image Clustering, a novel hierarchical clustering pipeline that leverages deep perceptual features to visualize and understand bias in unstructured and unlabeled datasets. It does so by effectively highlighting nuanced subcategories of information embedded within the data (such as multiple but repetitive shadow types) that typically are hard and/or expensive to annotate. Through experiments on a variety of image datasets, both open-source and internal, we demonstrate the effectiveness of deepPIC in (i) singling out errors in metadata from open-source datasets such as BDD100K;(ii) automatic nuanced metadata annotation;(iii) mining for edge cases;(iv) visualizing inherent bias both within and across multiple datasets;and (v) capturing synthetic data limitations;thus highlighting the wide variety of applications this pipeline can be applied to. All clustering results included here have been uploaded with image thumbnails on our project website - https://***/unpuzzle_dataset_bias/. We recommend zooming in for best impact.

关键词： Analytical models computer vision Visualization Annotations Pipelines Buildings Data visualization

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Coupling vision and Proprioception for Navigation of Legged Robots

Coupling Vision and Proprioception for Navigation of Legged ...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Fu, Zipeng Kumar, Ashish Agarwal, Ananye Qi, Haozhi Malik, Jitendra Pathak, Deepak Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665487399

We exploit the complementary strengths of vision and proprioception to develop a point-goal navigation system for legged robots, called VP-Nav. Legged systems are capable of traversing more complex terrain than wheeled robots, but to fully utilize this capability, we need a high-level path planner in the navigation system to be aware of the walking capabilities of the low-level locomotion policy in varying environments. We achieve this by using proprioceptive feedback to ensure the safety of the planned path by sensing unexpected obstacles like glass walls, terrain properties like slipperiness or softness of the ground and robot properties like extra payload that are likely missed by vision. The navigation system uses onboard cameras to generate an occupancy map and a corresponding cost map to reach the goal. A fast marching planner then generates a target path. A velocity command generator takes this as input to generate the desired velocity for the walking policy. A safety advisor module adds sensed unexpected obstacles to the occupancy map and environment-determined speed limits to the velocity command generator. We show superior performance compared to wheeled robot baselines, and ablation studies which have disjoint highlevel planning and low-level control. We also show the real-world deployment of VP-Nav on a quadruped robot with onboard sensors and computation. Videos at https://***

关键词： Legged locomotion Navigation Robot vision systems Robot sensing systems Generators Sensors Safety

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Remote Heart Rate Estimation by Signal Quality Attention Network

Remote Heart Rate Estimation by Signal Quality Attention Net...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Gao, Haoyuan Wu, Xiaopei Geng, Jidong Lv, Yang Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Heart rate estimation is very important for heart health monitoring. As a non-invasive optical technology, remote photoplethysmography (rPPG) has the advantages of non-contact, portability and low-price. However, motion and noise artifacts bring additional uncertainty to the results of heart rate estimation. Based on the signal quality assessment method, we propose a new remote heart estimation algorithm by signal quality attention mechanism and long short-term memory (LSTM) networks. The model consists of three parts: firstly, an LSTM network is used to estimate the heart rate sampling point by sampling point;secondly, a similar LSTM network predicts the signal quality;finally, an attention-based model uses the heart rates and quality scores predicted above to calculate the average heart rate of a period of time. The model allocates higher weights to the reliable heart rates estimated from high-quality signals, meanwhile, ignores unreliable results estimated from low-quality signals. Experiments show that LSTM with attention mechanism accurately estimates heart rate from corruption rPPG signal and it performs well on cross-subject tasks and cross-dataset tasks. The results also demonstrate that the scores predicted by the signal quality model is valuable to extract reliable heart rate.

关键词： Heart rate Training Uncertainty computer network reliability Estimation Predictive models Prediction algorithms

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Consistency-based Active Learning for Object Detection

Consistency-based Active Learning for Object Detection

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Yu, Weiping Zhu, Sijie Yang, Taojiannan Chen, Chen Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Active learning aims to improve the performance of the task model by selecting the most informative samples with a limited budget. Unlike most recent works that focus on applying active learning for image classification, we propose an effective Consistency-based Active Learning method for object Detection (CALD), which fully explores the consistency between the original and augmented data. CALD has three appealing benefits. (i) CALD is systematically designed by investigating the weaknesses of existing active learning methods, which do not take the unique challenges of object detection into account. (ii) CALD unifies box regression and classification with a single metric, which is not concerned with active learning methods for classification. CALD also focuses on the most informative local region rather than the whole image, which is beneficial for object detection. (iii) CALD not only gauges individual information for sample selection but also leverages mutual information to encourage a balanced data distribution. Extensive experiments show that CALD significantly outperforms existing state-of-the-art task-agnostic and detection-specific active learning methods on general object detection datasets. Based on the Faster R-CNN detector, CALD consistently surpasses the baseline method (random selection) by 2.9/2.8/0.8 mAP on average on PASCAL VOC 2007, PASCAL VOC 2012, and MS COCO.

关键词： Learning systems Measurement computer vision conferences Object detection Detectors pattern recognition

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Pose Estimation for Two-View Panoramas based on Keypoint Matching: a Comparative Study and Critical Analysis

Pose Estimation for Two-View Panoramas based on Keypoint Mat...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Murrugarra-Llerena, Jeffri da Silveira, Thiago L. T. Jung, Claudio R. Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Pose estimation is a crucial problem in several computer vision and robotics applications. For the two-view scenario, the typical pipeline consists of finding point correspondences between the two views and using them to estimate the pose. However, most available keypoint extraction and matching methods were designed to work with perspective images and may fail under not-affine distortions present in wide-angle or omnidirectional media, which are becoming increasingly popular in recent years. This paper presents a comprehensive comparative analysis of different keypoint matching algorithms for panoramas coupled to different linear and non-linear approaches for pose estimation. As an additional contribution, we explore a recent approach for mitigating spherical distortions using tangent plane projections, which can be coupled with any planar descriptor, and allows the adaptation of recent learning-based methods. We evaluate the combination of keypoint matching and pose estimation methods using the rotation and translation error of the estimated pose in different scenarios (indoor and outdoor), and our results indicate that SPHORB and "tangent SIFT" are competitive algorithms. We also show that tangent plane adaptations frequently present competitive results, and some optimization steps consistently improve the performance in all methods. We provide code at https://***/Artcs1/Keypoints

关键词： Learning systems Couplings computer vision Planarization Pose estimation Nonlinear distortion Pipelines

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