行人属性识别（Pedestrian Attribute Recognition，PAR）是以人为中心的研究中不可或缺的任务之一。然而，现有数据集忽略了不同域（如环境、时间、人群和数据源）的差异，仅采用简单的随机划分，且这些数据集上的模型性能已趋近饱和。过去五年间，尚无大规模数据集向公众开放。为解决这一问题，本文提出了一个全新的大规模跨域行人属性识别数据集MSP60K，以填补数据空白。该数据集包含8个场景下的60122张图像和57个属性标注，还进行了合成退化处理，以进一步缩小数据集与现实世界挑战性场景之间的差距。为建立更严谨的基准，我们在该数据集上采用随机划分和跨域划分两种协议，评估了17个代表性PAR模型的性能。此外，我们提出了一种创新的大语言模型（Large Language Model，LLM）增强PAR框架，命名为LLM-PAR。该框架通过Vision Transformer（ViT）骨干网络处理行人图像以提取特征，并引入多嵌入查询Transformer学习具有局部感知能力的特征用于属性分类。值得注意的是，我们利用LLM对该框架进行增强，实现集成学习和视觉特征增强。在多个PAR基准数据集上开展的综合实验充分验证了所提框架的有效性。本文配套的数据集和源代码将在https://github.com/Event-AHU/OpenPAR 公开发布。
英文摘要：
Pedestrian Attribute Recognition (PAR) is one of the indispensable tasks in human-centered research. However, existing datasets neglect different domains (e.g., environments, times, populations, and data sources), only conducting simple random splits, and the performance of these datasets has already approached saturation. In the past five years, no large-scale dataset has been opened to the public. To address this issue, this paper proposes a new large-scale, cross-domain pedestrian attribute recognition dataset to fill the data gap, termed MSP60K. It consists of 60,122 images and 57 attribute annotations across eight scenarios. Synthetic degradation is also conducted to further narrow the gap between the dataset and real-world challenging scenarios. To establish a more rigorous benchmark, we evaluate 17 representative PAR models under both random and cross-domain split protocols on our dataset. Additionally, we propose an innovative Large Language Model (LLM) augmented PAR framework, named LLM-PAR. This framework processes pedestrian images through a Vision Transformer (ViT) backbone to extract features and introduces a multi-embedding query Transformer to learn partial-aware features for attribute classification. Significantly, we enhance this framework with LLM for ensemble learning and visual feature augmentation. Comprehensive experiments across multiple PAR benchmark datasets have thoroughly validated the efficacy of our proposed framework. The dataset and source code accompanying this paper will be made publicly available at \url{https://github.com/Event-AHU/OpenPAR}.
Learning to recognize pedestrian attributes at far distance is a challenging problem in visual surveillance since face and body close-shots are hardly available; instead, only far-view image frames of pedestrian are given. In this study, we present an alternative approach that exploits the context of neighboring pedestrian images for improved attribute inference compared to the conventional SVM-based method. In addition, we conduct extensive experiments to evaluate the informativeness of background and foreground features for attribute recognition. Experiments are based on our newly released p…
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）
Pedestrian attribute recognition aims to assign multiple attributes to one pedestrian image captured by a video surveillance camera. Although numerous methods are proposed and make tremendous progress, we argue that it is time to step back and analyze the status quo of the area. We review and rethink the recent progress from three perspectives. First, given that there is no explicit and complete definition of pedestrian attribute recognition, we formally define and distinguish pedestrian attribute recognition from other similar tasks. Second, based on the proposed definition, we expose the lim…
In this paper, we first tackle the problem of pedestrian attribute recognition by video-based approach. The challenge mainly lies in spatial and temporal modeling and how to integrating them for effective and dynamic pedestrian representation. To solve this problem, a novel multi-task model based on the conventional neural network and temporal attention strategy is proposed. Since publicly available dataset is rare, two new large-scale video datasets with expanded attribute definition are presented, on which the effectiveness of both video-based pedestrian attribute recognition methods and the…
Spatio-Temporal Side Tuning Pre-trained Foundation Models for Video-based Pedestrian Attribute Recognition
School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；School of Artificial Intelligence, Anhui University（安徽大学人工智能学院）；Peng Cheng Laboratory（鹏城实验室）；Harbin Institute of Technology (HITSZ)（哈尔滨工业大学（深圳））；School of Computer Science, Peking University（北京大学计算机学院）；School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University（北京大学深圳研究生院电子与计算机工程学院）
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Fraunhofer IOSB（弗劳恩霍夫光电、系统技术及图像处理研究所）
Pedestrian attribute inference is a demanding problem in visual surveillance that can facilitate person retrieval, search and indexing. To exploit semantic relations between attributes, recent research treats it as a multi-label image classification task. The visual cues hinting at attributes can be strongly localized and inference of person attributes such as hair, backpack, shorts, etc., are highly dependent on the acquired view of the pedestrian. In this paper we assert this dependence in an end-to-end learning framework and show that a view-sensitive attribute inference is able to learn be…
VLM-PAR: A Vision Language Model for Pedestrian Attribute Recognition
Department of Innovation Engineering（创新工程系）；University of Salento, Italy（意大利萨伦托大学）；Institute of Applied Sciences and Intelligent Systems - CNR（应用科学与智能系统研究所 - CNR）；University of the Basque Country UPV/EHU（巴斯克国家大学UPV/EHU）；IKERBASQUE, Basque Foundation for Science（伊基塔斯克巴塞克基金会）；Sorbonne University Abu Dhabi（索邦大学阿布扎比分校）
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Fraunhofer IOSB（弗劳恩霍夫IOSB研究所）；Fraunhofer Center for Machine Learning（弗劳恩霍夫机器学习中心）
Recognizing soft-biometric pedestrian attributes is essential in video surveillance and fashion retrieval. Recent works show promising results on single datasets. Nevertheless, the generalization ability of these methods under different attribute distributions, viewpoints, varying illumination, and low resolutions remains rarely understood due to strong biases and varying attributes in current datasets. To close this gap and support a systematic investigation, we present UPAR, the Unified Person Attribute Recognition Dataset. It is based on four well-known person attribute recognition datasets…
Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；University of Sydney（悉尼大学）
This paper expands the strength of deep convolutional neural networks (CNNs) to the pedestrian attribute recognition problem by devising a novel attribute aware pooling algorithm. Existing vanilla CNNs cannot be straightforwardly applied to handle multi-attribute data because of the larger label space as well as the attribute entanglement and correlations. We tackle these challenges that hampers the development of CNNs for multi-attribute classification by fully exploiting the correlation between different attributes. The multi-branch architecture is adopted for fucusing on attributes at diffe…
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）
Despite various methods are proposed to make progress in pedestrian attribute recognition, a crucial problem on existing datasets is often neglected, namely, a large number of identical pedestrian identities in train and test set, which is not consistent with practical application. Thus, images of the same pedestrian identity in train set and test set are extremely similar, leading to overestimated performance of state-of-the-art methods on existing datasets. To address this problem, we propose two realistic datasets PETA\textsubscript{$zs$} and RAPv2\textsubscript{$zs$} following zero-shot se…
Tianjin University（天津大学）；China Academy of Launch Vehicle Technology（中国运载火箭技术研究院）
Pedestrian Attribute Recognition (PAR) is a challenging task in intelligent video surveillance. Two key challenges in PAR include complex alignment relations between images and attributes, and imbalanced data distribution. Existing approaches usually formulate PAR as a recognition task. Different from them, this paper addresses it as a decision-making task via a reinforcement learning framework. Specifically, PAR is formulated as a Markov decision process (MDP) by designing ingenious states, action space, reward function and state transition. To alleviate the inter-attribute imbalance problem,…
Pedestrian Attribute Recognition via CLIP based Prompt Vision-Language Fusion
School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；Information Materials and Intelligent Sensing Laboratory of Anhui Province（安徽省信息材料与智能感知实验室）；Anhui Provincial Key Laboratory of Multimodal Cognitive Computation（安徽省多模态认知计算重点实验室）；School of Artificial Intelligence, Anhui University（安徽大学人工智能学院）；Video Investigation Detachment of Hefei Public Security Bureau（合肥市公安局视频侦查支队）
PFM-VEPAR: Prompting Foundation Models for RGB-Event Camera based Pedestrian Attribute Recognition
City University of Macau（澳门城市大学）；Zhuhai College of Science and Technology（珠海科学技术学院）；Macau University of Science and Technology（澳门科学大学）；School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）
Instituto de Telecomunicações（电信研究所）；University of Beira Interior（贝拉内大学）；Technical University of Cluj-Napoca（克卢日-纳波卡技术大学）；TomiWorld
The automatic characterization of pedestrians in surveillance footage is a tough challenge, particularly when the data is extremely diverse with cluttered backgrounds, and subjects are captured from varying distances, under multiple poses, with partial occlusion. Having observed that the state-of-the-art performance is still unsatisfactory, this paper provides a novel solution to the problem, with two-fold contributions: 1) considering the strong semantic correlation between the different full-body attributes, we propose a multi-task deep model that uses an element-wise multiplication layer to…
Harbin Institute of Technology（哈尔滨工业大学）；Inner Mongolia University（内蒙古大学）
Thermal infrared (TIR) pedestrian tracking is one of the important components among numerous applications of computer vision, which has a major advantage: it can track pedestrians in total darkness. The ability to evaluate the TIR pedestrian tracker fairly, on a benchmark dataset, is significant for the development of this field. However, there is not a benchmark dataset. In this paper, we develop a TIR pedestrian tracking dataset for the TIR pedestrian tracker evaluation. The dataset includes 60 thermal sequences with manual annotations. Each sequence has nine attribute labels for the attribu…
Video Question Answering is a challenging problem in visual information retrieval, which provides the answer to the referenced video content according to the question. However, the existing visual question answering approaches mainly tackle the problem of static image question, which may be ineffectively for video question answering due to the insufficiency of modeling the temporal dynamics of video contents. In this paper, we study the problem of video question answering by modeling its temporal dynamics with frame-level attention mechanism. We propose the attribute-augmented attention networ…
Tianjin University（天津大学）；Chinese Academy of Sciences（中国科学院）
The discrimination and simplicity of features are very important for effective and efficient pedestrian detection. However, most state-of-the-art methods are unable to achieve good tradeoff between accuracy and efficiency. Inspired by some simple inherent attributes of pedestrians (i.e., appearance constancy and shape symmetry), we propose two new types of non-neighboring features (NNF): side-inner difference features (SIDF) and symmetrical similarity features (SSF). SIDF can characterize the difference between the background and pedestrian and the difference between the pedestrian contour and…
Application of Vision-Language Model to Pedestrians Behavior and Scene Understanding in Autonomous Driving
ECE Department（电子工程系）；Carnegie Mellon University（卡内基梅隆大学）；Computer Science Department（计算机科学系）；Columbia University（哥伦比亚大学）；Rotman School of Management（罗特曼管理学院）；University of Toronto（多伦多大学）；Department of Statistics（统计学系）；George Washington University（乔治华盛顿大学）；Department of Computer Science（计算机科学系）；San Francisco State University（旧金山州立大学）
PFSD: A Multi-Modal Pedestrian-Focus Scene Dataset for Rich Tasks in Semi-Structured Environments
School of Information Science and Technology, University of Science and Technology of China（中国科学技术大学信息科学技术学院）；State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), CASIA（中国科学院自动化研究所多模态人工智能系统国家重点实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
We tackle the challenge of reliably and automatically localizing pedestrians in real-life conditions through overhead depth imaging at unprecedented high-density conditions. Leveraging upon a combination of Histogram of Oriented Gradients-like feature descriptors, neural networks, data augmentation and custom data annotation strategies, this work contributes a robust and scalable machine learning-based localization algorithm, which delivers near-human localization performance in real-time, even with local pedestrian density of about 3 ped/m2, a case in which most state-of-the art algorithms de…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Temple University（天普大学）
In this paper, we aim to improve the dataset foundation for pedestrian attribute recognition in real surveillance scenarios. Recognition of human attributes, such as gender, and clothes types, has great prospects in real applications. However, the development of suitable benchmark datasets for attribute recognition remains lagged behind. Existing human attribute datasets are collected from various sources or an integration of pedestrian re-identification datasets. Such heterogeneous collection poses a big challenge on developing high quality fine-grained attribute recognition algorithms. Furth…
Pedestrian Attribute Recognition (PAR) has aroused extensive attention due to its important role in video surveillance scenarios. In most cases, the existence of a particular attribute is strongly related to a partial region. Recent works design complicated modules, e.g., attention mechanism and proposal of body parts to localize the attribute corresponding region. These works further prove that localization of attribute specific regions precisely will help in improving performance. However, these part-information-based methods are still not accurate as well as increasing model complexity whic…
Pedestrian detection is an initial step to perform outdoor scene analysis, which plays an essential role in many real-world applications. Although having enjoyed the merits of deep learning frameworks from the generic object detectors, pedestrian detection is still a very challenging task due to heavy occlusion and highly crowded group. Generally, the conventional detectors are unable to differentiate individuals from each other effectively under such a dense environment. To tackle this critical problem, we propose an attribute-aware pedestrian detector to explicitly model people's semantic at…
Forecasting the trajectory of pedestrians in shared urban traffic environments is still considered one of the challenging problems facing the development of autonomous vehicles (AVs). In the literature, this problem is often tackled using recurrent neural networks (RNNs). Despite the powerful capabilities of RNNs in capturing the temporal dependency in the pedestrians' motion trajectories, they were argued to be challenged when dealing with longer sequential data. Thus, in this work, we are introducing a framework based on the transformer networks that were shown recently to be more efficient …
State-of-the-art methods treat pedestrian attribute recognition as a multi-label image classification problem. The location information of person attributes is usually eliminated or simply encoded in the rigid splitting of whole body in previous work. In this paper, we formulate the task in a weakly-supervised attribute localization framework. Based on GoogLeNet, firstly, a set of mid-level attribute features are discovered by novelly designed detection layers, where a max-pooling based weakly-supervised object detection technique is used to train these layers with only image-level labels with…
City University of Hong Kong（香港城市大学）；The Chinese University of Hong Kong（香港中文大学）；SenseTime Research（商汤科技研究院）
Pedestrian attribute recognition has attracted many attentions due to its wide applications in scene understanding and person analysis from surveillance videos. Existing methods try to use additional pose, part or viewpoint information to complement the global feature representation for attribute classification. However, these methods face difficulties in localizing the areas corresponding to different attributes. To address this problem, we propose a novel Localization Guided Network which assigns attribute-specific weights to local features based on the affinity between proposals pre-extract…
Tsinghua University（清华大学）；Beihang University（北京航空航天大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Pedestrian attribute recognition has been an emerging research topic in the area of video surveillance. To predict the existence of a particular attribute, it is demanded to localize the regions related to the attribute. However, in this task, the region annotations are not available. How to carve out these attribute-related regions remains challenging. Existing methods applied attribute-agnostic visual attention or heuristic body-part localization mechanisms to enhance the local feature representations, while neglecting to employ attributes to define local feature areas. We propose a flexible…
Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition
School of Artificial Intelligence, Anhui University（安徽大学人工智能学院）；School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；National University of Defense Technology（国防科学技术大学）；Harbin Institute of Technology（哈尔滨工业大学）；Peng Cheng Laboratory（鹏城实验室）
Tsinghua University（清华大学）；University of Warwick（华威大学）；Xidian University（西安电子科技大学）
Pedestrian attribute recognition has received increasing attention due to its important role in video surveillance applications. However, most existing methods are designed for a fixed set of attributes. They are unable to handle the incremental few-shot learning scenario, i.e. adapting a well-trained model to newly added attributes with scarce data, which commonly exists in the real world. In this work, we present a meta learning based method to address this issue. The core of our framework is a meta architecture capable of disentangling multiple attribute information and generalizing rapidly…
Queen Mary University of London（伦敦玛丽女王大学）；Vision Semantics Ltd（视觉语义有限公司）
Recognising semantic pedestrian attributes in surveillance images is a challenging task for computer vision, particularly when the imaging quality is poor with complex background clutter and uncontrolled viewing conditions, and the number of labelled training data is small. In this work, we formulate a Joint Recurrent Learning (JRL) model for exploring attribute context and correlation in order to improve attribute recognition given small sized training data with poor quality images. The JRL model learns jointly pedestrian attribute correlations in a pedestrian image and in particular their se…
Self-driving vehicles are the future of transportation. With current advancements in this field, the world is getting closer to safe roads with almost zero probability of having accidents and eliminating human errors. However, there is still plenty of research and development necessary to reach a level of robustness. One important aspect is to understand a scene fully including all details. As some characteristics (attributes) of objects in a scene (drivers' behavior for instance) could be imperative for correct decision making. However, current algorithms suffer from low-quality datasets with…
Pedestrians are arguably one of the most safety-critical road users to consider for autonomous vehicles in urban areas. In this paper, we address the problem of jointly detecting pedestrians and recognizing 32 pedestrian attributes from a single image. These encompass visual appearance and behavior, and also include the forecasting of road crossing, which is a main safety concern. For this, we introduce a Multi-Task Learning (MTL) model relying on a composite field framework, which achieves both goals in an efficient way. Each field spatially locates pedestrian instances and aggregates attribu…
AttMOT: Improving Multiple-Object Tracking by Introducing Auxiliary Pedestrian Attributes
Institute of Software Chinese Academy of Science（中国科学院软件研究所）；Turing Quantum Company（图灵量子公司）；OPPO Research Institute（OPPO研究院）；University of North Texas（北德克萨斯大学）
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）
While recent studies on pedestrian attribute recognition have shown remarkable progress in leveraging complicated networks and attention mechanisms, most of them neglect the inter-image relations and an important prior: spatial consistency and semantic consistency of attributes under surveillance scenarios. The spatial locations of the same attribute should be consistent between different pedestrian images, \eg, the ``hat" attribute and the ``boots" attribute are always located at the top and bottom of the picture respectively. In addition, the inherent semantic feature of the ``hat" attribute…
Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）
Learning to predict multiple attributes of a pedestrian is a multi-task learning problem. To share feature representation between two individual task networks, conventional methods like Cross-Stitch and Sluice network learn a linear combination of features or feature subspaces. However, linear combination rules out the complex interdependency between channels. Moreover, spatial information exchanging is less-considered. In this paper, we propose a novel Co-Attentive Sharing (CAS) module which extracts discriminative channels and spatial regions for more effective feature sharing in multi-task …
