Accurate prediction of pedestrian trajectories is crucial for improving the safety of autonomous driving. However, this task is generally nontrivial due to the inherent stochasticity of human motion, which naturally requires the predictor to generate multi-modal prediction. Previous works leverage various generative methods, such as GAN and VAE, for pedestrian trajectory prediction. Nevertheless, these methods may suffer from mode collapse and relatively low-quality results. The denoising diffusion probabilistic model (DDPM) has recently been applied to trajectory prediction due to its simple training process and powerful reconstruction ability. However, current diffusion-based methods do not fully utilize input information and usually require many denoising iterations that lead to a long inference time or an additional network for initialization. To address these challenges and facilitate the use of diffusion models in multi-modal trajectory prediction, we propose GDTS, a novel Goal-Guided Diffusion Model with Tree Sampling for multi-modal trajectory prediction. Considering the "goal-driven" characteristics of human motion, GDTS leverages goal estimation to guide the generation of the diffusion network. A two-stage tree sampling algorithm is presented, which leverages common features to reduce the inference time and improve accuracy for multi-modal prediction. Experimental results demonstrate that our proposed framework achieves comparable state-of-the-art performance with real-time inference speed in public datasets.
Rutgers University（罗格斯大学）；Toyota Motor North America（丰田汽车北美公司）
In Smart City and Vehicle-to-Everything (V2X) systems, acquiring pedestrians' accurate locations is crucial to traffic safety. Current systems adopt cameras and wireless sensors to detect and estimate people's locations via sensor fusion. Standard fusion algorithms, however, become inapplicable when multi-modal data is not associated. For example, pedestrians are out of the camera field of view, or data from camera modality is missing. To address this challenge and produce more accurate location estimations for pedestrians, we propose a Generative Adversarial Network (GAN) architecture. During…
This paper proposes a novel approach for predicting the motion of pedestrians interacting with others. It uses a Generative Adversarial Network (GAN) to sample plausible predictions for any agent in the scene. As GANs are very susceptible to mode collapsing and dropping, we show that the recently proposed Info-GAN allows dramatic improvements in multi-modal pedestrian trajectory prediction to avoid these issues. We also left out L2-loss in training the generator, unlike some previous works, because it causes serious mode collapsing though faster convergence. We show through experiments on real…
Understanding and predicting the intention of pedestrians is essential to enable autonomous vehicles and mobile robots to navigate crowds. This problem becomes increasingly complex when we consider the uncertainty and multimodality of pedestrian motion, as well as the implicit interactions between members of a crowd, including any response to a vehicle. Our approach, Probabilistic Crowd GAN, extends recent work in trajectory prediction, combining Recurrent Neural Networks (RNNs) with Mixture Density Networks (MDNs) to output probabilistic multimodal predictions, from which likely modal paths a…
Tokyo Institute of Technology（东京工业大学）；NTT Corporation（日本电报电话公司）
The combined use of multiple modalities enables accurate pedestrian detection under poor lighting conditions by using the high visibility areas from these modalities together. The vital assumption for the combination use is that there is no or only a weak misalignment between the two modalities. In general, however, this assumption often breaks in actual situations. Due to this assumption's breakdown, the position of the bounding boxes does not match between the two modalities, resulting in a significant decrease in detection accuracy, especially in regions where the amount of misalignment is …
MS-DETR: Multispectral Pedestrian Detection Transformer with Loosely Coupled Fusion and Modality-Balanced Optimization
School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；Research Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）
VI-Diff: Unpaired Visible-Infrared Translation Diffusion Model for Single Modality Labeled Visible-Infrared Person Re-identification
University of Chinese Academy of Sciences (UCAS)（中国科学院大学）；The Center for Research on Intelligent Perception and Computing (CRIPAC)（智能感知与计算研究中心）；Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）
University of Trento（特伦托大学）；The Chinese University of Hong Kong（香港中文大学）；The University of Sydney（悉尼大学）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）；University of Perugia（佩鲁贾大学）
This paper presents a novel method for detecting pedestrians under adverse illumination conditions. Our approach relies on a novel cross-modality learning framework and it is based on two main phases. First, given a multimodal dataset, a deep convolutional network is employed to learn a non-linear mapping, modeling the relations between RGB and thermal data. Then, the learned feature representations are transferred to a second deep network, which receives as input an RGB image and outputs the detection results. In this way, features which are both discriminative and robust to bad illumination …
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）
Kunming University of Science and Technology（昆明理工大学）
Multispectral pedestrian detection is a technology designed to detect and locate pedestrians in Color and Thermal images, which has been widely used in automatic driving, video surveillance, etc. So far most available multispectral pedestrian detection algorithms only achieved limited success in pedestrian detection because of the lacking take into account the confusion of pedestrian information and background noise in Color and Thermal images. Here we propose a multispectral pedestrian detection algorithm, which mainly consists of a cascaded information enhancement module and a cross-modal at…
Pedestrian detection benefits greatly from deep convolutional neural networks (CNNs). However, it is inherently hard for CNNs to handle situations in the presence of occlusion and scale variation. In this paper, we propose W$^3$Net, which attempts to address above challenges by decomposing the pedestrian detection task into \textbf{\textit{W}}here, \textbf{\textit{W}}hat and \textbf{\textit{W}}hether problem directing against pedestrian localization, scale prediction and classification correspondingly. Specifically, for a pedestrian instance, we formulate its feature by three steps. i) We gene…
Pedestrian behavior prediction is one of the major challenges for intelligent driving systems in urban environments. Pedestrians often exhibit a wide range of behaviors and adequate interpretations of those depend on various sources of information such as pedestrian appearance, states of other road users, the environment layout, etc. To address this problem, we propose a novel multi-modal prediction algorithm that incorporates different sources of information captured from the environment to predict future crossing actions of pedestrians. The proposed model benefits from a hybrid learning arch…
Predicting pedestrian behavior is a crucial task for intelligent driving systems. Accurate predictions require a deep understanding of various contextual elements that potentially impact the way pedestrians behave. To address this challenge, we propose a novel framework that relies on different data modalities to predict future trajectories and crossing actions of pedestrians from an ego-centric perspective. Specifically, our model utilizes a cross-modal Transformer architecture to capture dependencies between different data types. The output of the Transformer is augmented with representation…
Mercedes-Benz AG（梅赛德斯-奔驰股份公司）；University of Stuttgart（斯图加特大学）
This paper presents a novel approach to pedestrian trajectory prediction for on-board camera systems, which utilizes behavioral features of pedestrians that can be inferred from visual observations. Our proposed method, called Behavior-Aware Pedestrian Trajectory Prediction (BA-PTP), processes multiple input modalities, i.e. bounding boxes, body and head orientation of pedestrians as well as their pose, with independent encoding streams. The encodings of each stream are fused using a modality attention mechanism, resulting in a final embedding that is used to predict future bounding boxes in t…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Renmin University of China（中国人民大学）；CEBSIT, Chinese Academy of Sciences（中国科学院CEBSIT）
Multispectral pedestrian detection has shown great advantages under poor illumination conditions, since the thermal modality provides complementary information for the color image. However, real multispectral data suffers from the position shift problem, i.e. the color-thermal image pairs are not strictly aligned, making one object has different positions in different modalities. In deep learning based methods, this problem makes it difficult to fuse the feature maps from both modalities and puzzles the CNN training. In this paper, we propose a novel Aligned Region CNN (AR-CNN) to handle the w…
Pedestrian trajectory prediction is an essential task in robotic applications such as autonomous driving and robot navigation. State-of-the-art trajectory predictors use a conditional variational autoencoder (CVAE) with recurrent neural networks (RNNs) to encode observed trajectories and decode multi-modal future trajectories. This process can suffer from accumulated errors over long prediction horizons (>=2 seconds). This paper presents BiTraP, a goal-conditioned bi-directional multi-modal trajectory prediction method based on the CVAE. BiTraP estimates the goal (end-point) of trajectories an…
MulCPred: Learning Multi-modal Concepts for Explainable Pedestrian Action Prediction
Graduate School of Informatics, Nagoya University（名古屋大学情报学研究科）；Institutes of Innovation for Future Society, Nagoya University（名古屋大学未来社会创新机构）；Graduate School of Engineering, Gifu University（岐阜大学工学研究科）；Tier IV Inc.（Tier IV公司）
Modeling the dynamics of people walking is a problem of long-standing interest in computer vision. Many previous works involving pedestrian trajectory prediction define a particular set of individual actions to implicitly model group actions. In this paper, we present a novel architecture named GP-Graph which has collective group representations for effective pedestrian trajectory prediction in crowded environments, and is compatible with all types of existing approaches. A key idea of GP-Graph is to model both individual-wise and group-wise relations as graph representations. To do this, GP-G…
Pedestrian behavior prediction is one of the major challenges for intelligent driving systems. Pedestrians often exhibit complex behaviors influenced by various contextual elements. To address this problem, we propose BiPed, a multitask learning framework that simultaneously predicts trajectories and actions of pedestrians by relying on multimodal data. Our method benefits from 1) a bifold encoding approach where different data modalities are processed independently allowing them to develop their own representations, and jointly to produce a representation for all modalities using shared param…
Predicting the behavior of road users, particularly pedestrians, is vital for safe motion planning in the context of autonomous driving systems. Traditionally, pedestrian behavior prediction has been realized in terms of forecasting future trajectories. However, recent evidence suggests that predicting higher-level actions, such as crossing the road, can help improve trajectory forecasting and planning tasks accordingly. There are a number of existing datasets that cater to the development of pedestrian action prediction algorithms, however, they lack certain characteristics, such as bird's ey…
MUSCLE-NET: Predicted-Multiscale-Aware Network for Pedestrian Trajectory Forecasting
Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology, School of Automation and Intelligent Manufacturing, Southern University of Science and Technology (SUSTech), Shenzhen（广东省全主动系统控制理论与技术重点实验室，自动化与智能制造学院，南方科技大学（SUSTech），深圳）；Department of Mechanical Engineering, City University of Hong Kong, Hong Kong SAR, China（香港城市大学机械工程系，香港特别行政区，中国）
Sparse Prototype Network for Explainable Pedestrian Behavior Prediction
Institutes of Innovation for Future Society, Nagoya University（名古屋大学未来社会创新研究院）；Graduate School of Engineering, Gifu University（岐阜大学工学研究科）；Tier IV Inc.（Tier IV公司）；Nagoya University Open Innovation Center（名古屋大学开放创新中心）；Graduate School of Informatics, Nagoya University（名古屋大学信息学研究科）
Zhejiang University（浙江大学）；University of Twente（特温特大学）
Effective fusion of complementary information captured by multi-modal sensors (visible and infrared cameras) enables robust pedestrian detection under various surveillance situations (e.g. daytime and nighttime). In this paper, we present a novel box-level segmentation supervised learning framework for accurate and real-time multispectral pedestrian detection by incorporating features extracted in visible and infrared channels. Specifically, our method takes pairs of aligned visible and infrared images with easily obtained bounding box annotations as input and estimates accurate prediction map…
This paper presents a novel framework for human trajectory prediction based on multimodal data (video and radar). Motivated by recent neuroscience discoveries, we propose incorporating a structured memory component in the human trajectory prediction pipeline to capture historical information to improve performance. We introduce structured LSTM cells for modelling the memory content hierarchically, preserving the spatiotemporal structure of the information and enabling us to capture both short-term and long-term context. We demonstrate how this architecture can be extended to integrate salient …
Three-Step Hierarchical Transformer for Multi-Pedestrian Trajectory Prediction
IMT Nord Europe, University of Lille, CNRS UMR 9189 - CRIStAL（IMT 北欧洲、里尔大学、法国国家科学研究中心 UMR 9189 - CRIStAL）；University of Lille, CNRS UMR 9189 - CRIStAL（里尔大学、法国国家科学研究中心 UMR 9189 - CRIStAL）；Explain
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB（弗劳恩霍夫光电子、系统技术与图像利用研究所 IOSB）
Recurrent neural networks are able to learn complex long-term relationships from sequential data and output a pdf over the state space. Therefore, recurrent models are a natural choice to address path prediction tasks, where a trained model is used to generate future expectations from past observations. When applied to security applications, like predicting the path of pedestrians for risk assessment, a point-wise greedy (ML) evaluation of the output pdf is not feasible, since the environment often allows multiple choices. Therefore, a robust risk assessment has to take all options into accoun…
DeepIPCv3: Event-Aware Multi-Modal Sensor Fusion for Sudden Pedestrian Crossing Avoidance
Department of Computer Science and Electronics, Universitas Gadjah Mada（计算机科学与电子系，加雅马达大学）；Department of Computer Science and Engineering, Toyohashi University of Technology（计算机科学与工程系，东福士大学）
FitPro: A Zero-Shot Framework for Interactive Text-based Pedestrian Retrieval in Open World
Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University（教育区块链与智能技术重点实验室，教育部，广西师范大学）；Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University（广西多源信息挖掘与安全重点实验室，广西师范大学）
GTransPDM: A Graph-embedded Transformer with Positional Decoupling for Pedestrian Crossing Intention Prediction
Department of Traffic Information and Control Engineering, Jilin University（交通信息与控制工程系，吉林大学）；BIT-Barcelona Innovative Transportation Research Group, Civil Engineering School, UPC Barcelona Tech（巴塞罗那创新交通研究组，土木工程学院，UPC巴塞罗那技术学院）；Computer Vision Center (CVC), Computer Science Department, Universitat Autònoma de Barcelona (UAB)（计算机视觉中心（CVC），计算机科学系，巴塞罗那自治大学（UAB））
针对行人位置畸变及多模态时序建模难题，本文提出 GTransPDM，通过位置解耦与图嵌入 Transformer 融合姿态、位置和车辆运动特征，在 PIE 和 JAAD 上取得最优性能。
Human intention prediction is a growing area of research where an activity in a video has to be anticipated by a vision-based system. To this end, the model creates a representation of the past, and subsequently, it produces future hypotheses about upcoming scenarios. In this work, we focus on pedestrians' early intention prediction in which, from a current observation of an urban scene, the model predicts the future activity of pedestrians that approach the street. Our method is based on a multi-modal transformer that encodes past observations and produces multiple predictions at different an…
TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations
Perception, Robotics and Intelligent Machines Research Group (PRIME) Department of Computer Science, Université de Moncton, Canada（感知、机器人与智能机器研究组（PRIME）计算机科学系，新不伦瑞克大学）
Changzhou University（常州大学）；University of California, Berkeley（加州大学伯克利分校）；Nanjing Xiaozhuang University（南京晓庄学院）
Pedestrian trajectory prediction in dynamic scenes remains a challenging and critical problem in numerous applications, such as self-driving cars and socially aware robots. Challenges concentrate on capturing pedestrians' motion patterns and social interactions, as well as handling the future uncertainties. Recent studies focus on modeling pedestrians' motion patterns with recurrent neural networks, capturing social interactions with pooling-based or graph-based methods, and handling future uncertainties by using random Gaussian noise as the latent variable. However, they do not integrate spec…
GoalNet: Goal Areas Oriented Pedestrian Trajectory Prediction
Department of Electronic Engineering, National Taipei University of Technology（电子工程系，国立台北科技大学）；Department of Artificial Intelligence Technology, National Taipei University of Technology（人工智能技术系，国立台北科技大学）
École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
Forecasting pedestrians' future motions is essential for autonomous driving systems to safely navigate in urban areas. However, existing prediction algorithms often overly rely on past observed trajectories and tend to fail around abrupt dynamic changes, such as when pedestrians suddenly start or stop walking. We suggest that predicting these highly non-linear transitions should form a core component to improve the robustness of motion prediction algorithms. In this paper, we introduce the new task of pedestrian stop and go forecasting. Considering the lack of suitable existing datasets for it…
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Toyota Motor Europe（丰田汽车欧洲公司）；CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）
Accurate prediction of pedestrian and bicyclist paths is integral to the development of reliable autonomous vehicles in dense urban environments. The interactions between vehicle and pedestrian or bicyclist have a significant impact on the trajectories of traffic participants e.g. stopping or turning to avoid collisions. Although recent datasets and trajectory prediction approaches have fostered the development of autonomous vehicles yet the amount of vehicle-pedestrian (bicyclist) interactions modeled are sparse. In this work, we propose Euro-PVI, a dataset of pedestrian and bicyclist traject…
FlexiReID: Adaptive Mixture of Expert for Multi-Modal Person Re-Identification
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（多媒体可信感知与高效计算重点实验室，中国教育部，厦门大学）；National University of Singapore（新加坡国立大学）；Tencent YouTu Lab（腾讯优图实验室）；Institute of Artificial Intelligence,Xiamen University（人工智能研究院，厦门大学）
Xi’an Jiaotong University（西安交通大学）；JD Finance America Corporation（京东金融美国公司）；Wormpex AI Research（Wormpex人工智能研究院）
Understanding the multiple socially-acceptable future behaviors is an essential task for many vision applications. In this paper, we propose a tree-based method, termed as Social Interpretable Tree (SIT), to address this multi-modal prediction task, where a hand-crafted tree is built depending on the prior information of observed trajectory to model multiple future trajectories. Specifically, a path in the tree from the root to leaf represents an individual possible future trajectory. SIT employs a coarse-to-fine optimization strategy, in which the tree is first built by high-order velocity to…
