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With the unprecedented shift towards automated urban environments in recent years, a new paradigm is required to study pedestrian behaviour. Studying pedestrian behaviour in futuristic scenarios requires modern data sources that consider both the Automated Vehicle (AV) and pedestrian perspectives. Current open datasets on AVs predominantly fail to account for the latter, as they do not include an adequate number of events and associated details that involve pedestrian and vehicle interactions. To address this issue, we propose using Virtual Reality (VR) data as a complementary resource to current datasets, which can be designed to measure pedestrian behaviour under specific conditions. In this research, we focus on the context-aware pedestrian trajectory prediction framework for automated vehicles at mid-block unsignalized crossings. For this purpose, we develop a novel multi-input network of Long Short-Term Memory (LSTM) and fully connected dense layers. In addition to past trajectories, the proposed framework incorporates pedestrian head orientations and distance to the upcoming vehicles as sequential input data. By merging the sequential data with contextual information of the environment, we train a model to predict the future pedestrian trajectory. Our results show that the prediction error is reduced by considering contextual information extracted from the crossing environment, as well as the addition of time-series behavioural information to the model. To analyse the application of the methods to real AV data, the proposed framework is trained and applied to pedestrian trajectories extracted from an open-access video dataset. Finally, by implementing a game theory-based model interpretability method, we provide detailed insights and propose recommendations to improve the current automated vehicle sensing systems from a pedestrian-oriented point of view.We use a novel conditional context loss to train the whole network end-to-end to make predictions in line with social and physical rules.Track current CasinoCoin prices in real-time with historical CSC USD charts, liquidity, and volume. Get top exchanges, markets, and more.

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State change. (d)–(f) show our model misses the correct direction since pedestrians change their motion states during the

This work was supported partially by the National Natural Science Foundation of China (Grant No. 62172177, 62101179 and 61571205), in part by Natural Science Foundation of Hubei Province (Grant No. 2023CFB332), and in part by the Fundamental Research Funds for the Central Universities (Grant No. 2023yjsCXCY040, 2023yjsCXCY014 and 2023JYCXJJ037).

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In this work, we aim to investigate the differences in interactive behaviors and semantic differences. Besides, we try to eliminate the differences through a specific transfer network to achieve effective contextual modeling for the future activities of agents. Therefore, we propose a contextual semantic consistency network (CSCNet) to cross these gaps. First, we use a context-aware transfer sub-network to obtain the intermediate representations describing social and physical interactions. Second, we transfer these data with distribution shifts and different manifestations to a common feature space to eliminate semantic differences. Moreover, the multiple modules are trained end-to-end using a multi-target loss function that penalizes average error, intermediate representation accuracy, and prediction consistency with the generated prior. After that, we will predict agents’ future trajectories under the corresponding priors to make predictions conforming to social rules and physical constraints.

Conghao Wong is currently a full-time MSc student in the School of Electronic Information and Communications, Huazhong University of Sciences and Technology (HUST), China. He received the BE degree in China University of Geosciences (CUG), Wuhan, China, 2023. His current research interests include video analysis and understanding.

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por B Xia · 2023 · Mencionado por 13 — Title:CSCNet: Contextual Semantic Consistency Network for Trajectory Prediction in Crowded Spaces ... Abstract: Trajectory prediction aims to ...

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However, they ignore the Semantic Shift Phenomenon when modeling these interactions in various prediction sceneries. There exist several kinds of semantic deviations inner or between social and physical interactions, which we call the “Gap”. For example, the park’s lawn allows for walking and resting, but few pedestrians attempt to do so on the lawn beside sidewalks. This is a gap between the theoretical meaning and the actual behavior, which also exists in social and physical interactions. Social interaction and physical interaction reflect the same thing from two different perspectives. The depiction of two interactions are expected to work together to model agents’ interactive behaviors. However, most previous methods employ different forms of features to describe these interactive behaviors without any normalization or regulation operations. Besides, few of them further align them semantically, leading to another kind of “Gap”, which affects the performance of prediction models. This makes it difficult for the two semantics to work as they should.

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Combining the context-aware transfer and conditional context loss, CSCNet outperforms the existing models on ETH-UCY and SDD Datasets.

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View more referencesCited by (12)Under the hood of transformer networks for trajectory forecasting2023, Pattern RecognitionShow abstractTransformer Networks have established themselves as the de-facto state-of-the-art for trajectory forecasting but there is currently no systematic study on their capability to model the motion patterns of people, without interactions with other individuals nor the social context. There is abundant literature on LSTMs, CNNs and GANs on this subject. However methods adopting Transformer techniques achieve great performances by complex models and a clear analysis of their adoption as plain sequence models is missing.

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The rest of the paper is organized as follows: We give a brief overview of related work in Section 2. In Section 3, we will describe our model in detail. We show the experimental analysis of CSCNet in Section 4. We also discuss the limitations of CSCNet in Section 5.

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Recurrent neural networks have shown good abilities in learning the spatio-temporal dependencies of moving agents in crowded scenes. Recently, they have been adopted to predict the motion of pedestrians by learning the relative motion of each individual in the crowd with respect to its neighbors. Crowded scenes present a wide variety of situations, which do not depend solely on the agents’ positions, but also relate to the structure of the environment, the density of the crowd, and the social relationships between pedestrians. In this work we propose a framework to improve the state-of-the-art models of crowd motion prediction by enriching the learning model with the social relationships between pedestrians walking in the crowd, as well as the layout of the environment. We observe that socially-related people tend to exhibit coherent motion patterns. Exploiting the motion coherency, we are able to cluster trajectories with similar motion properties and improve the trajectory prediction, especially at the group level. Furthermore, we incorporate into the model also the layout of the environment, to guarantee a more realistic and reliable learning framework. We evaluate our approach on standard crowd benchmark datasets, demonstrating its efficacy and applicability, improving the accuracy in trajectory prediction.

In crowded spaces, agents’ short-term behaviors or activities may be easily influenced by frequent socially interactive behaviors and the surroundings. Previous methods like [15], [16] have classified these two factors that may affect agents’ future activities into Social Interaction i.e., agent-agent interaction, and Physical Interaction i.e., agent-scene interaction. Moreover, a lot of networks or modules [17], [18] are employed to obtain the corresponding activity semantics and scene semantics, respectively. For instance, SoPhie [9], and S-BiGAT [10] use Convolutional Neural Networks (CNNs) to extract visual features of scene images (scene semantics) on one side, and design another attention-based social module to obtain the activity semantics on the other side. Outstanding performance can be achieved with the joint efforts of these two different interactive cues.

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Sparse additive models have shown competitive performance for high-dimensional variable selection and prediction due to their representation flexibility and interpretability. Despite their theoretical properties have been studied extensively, few works have addressed the robustness for the sparse additive models. In this paper, we employ the robust average top-k (ATk) loss as classification error measure and propose a new sparse algorithm, named ATk group sparse additive machine (ATk-GSAM). Besides the robust concern, the ATk-GSAM has well adaptivity by integrating the data dependent hypothesis space and group sparse regularizer together. Generalization error bound is established by the concentration estimate with empirical covering numbers. In particular, our error analysis shows that ATk-GSAM can achieve the learning rate O(n−1/2) under appropriate conditions. We further analyze the robustness of ATk-GSAM via a sample-weighted procedure interpretation, and the theoretical guarantees on grouped variable selection. Experimental evaluations on both simulated and benchmark datasets validate the effectiveness and robustness of the new algorithm.

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View all citing articles on ScopusRecommended articles (6)Research articleIncremental Fisher linear discriminant based on data denoisingKnowledge-Based Systems, Volume 237, 2023, Article 107799Show abstractIn this article we consider Incremental Fisher linear discriminant (IFLD) based on data denoising. The data denoising is completed by Markov sampling such that the generated non-noise sample sequence is an uniformly ergodic Markov chain (u.e.M.c.). We first establish the generalization bounds of IFLD with u.e.M.c. samples, and prove that the IFLD algorithm with u.e.M.c. samples is consistent. We also present two new IFLD classification algorithms based on Markov sampling, IFLD based on Markov sampling (IFLD-MS) and improved IFLD based on Markov sampling (IIFLD-MS). Experimental results of benchmark repository suggest that IFLD-MS and IIFLD-MS have better performance than the classical IFLD, the incremental support vector machine (ISVM) and other IFLD algorithms.

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Qinmu Peng received the PhD degree from the Department of computer Science, Hong Kong Baptist University, Hong Kong, in 2015. He is currently an Assistant Professor with the School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, Hubei, China. His current research interests include medical image processing, pattern recognition, machine learning, and computer vision.

Predicting pedestrian trajectory is an essential task in many applications. While previous studies based on graphs seek to model spatiotemporal information among pedestrian interactions, most of them neglect the recursive and continuous relations between neighboring time points. In this paper, we propose an evolving spatiotemporal graph attention network to predict future trajectories of pedestrians. This model considers the evolving relations of social interactions between contiguous time points and uses coordinates. The interaction is modeled by an evolving and dynamic attention mechanism. The social influence of each pedestrians of current frame is evolved from that of last frame and will be utilized to generate the social influence of next frame. The proposed model was tested on two challenging datasets and the experimental results prove the strength of the model.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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CSCNet. Code for the paper "Rethinking the CSC Model for Natural Images", which has been accepted to NeurIPS 2023. Instructions to reproduce our results:.

As humans we possess an intuitive ability for navigation which we master through years of practice; however existing approaches to model this trait for diverse tasks including monitoring pedestrian flow and detecting abnormal events have been limited by using a variety of hand-crafted features. Recent research in the area of deep-learning has demonstrated the power of learning features directly from the data; and related research in recurrent neural networks has shown exemplary results in sequence-to-sequence problems such as neural machine translation and neural image caption generation. Motivated by these approaches, we propose a novel method to predict the future motion of a pedestrian given a short history of their, and their neighbours, past behaviour. The novelty of the proposed method is the combined attention model which utilises both “soft attention” as well as “hard-wired” attention in order to map the trajectory information from the local neighbourhood to the future positions of the pedestrian of interest. We illustrate how a simple approximation of attention weights (i.e. hard-wired) can be merged together with soft attention weights in order to make our model applicable for challenging real world scenarios with hundreds of neighbours. The navigational capability of the proposed method is tested on two challenging publicly available surveillance databases where our model outperforms the current-state-of-the-art methods. Additionally, we illustrate how the proposed architecture can be directly applied for the task of abnormal event detection without handcrafting the features.

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Beihao Xia is currently pursuing the PhD degree in the School of Electronic Information and Communications (EIC), Huazhong University of Sciences and Technology (HUST), China. He received the BE degree in College of Computer Science and Electronic Engineering, Hunan University (HNU), China, in 2015, and the MSc degree in the School of EIC, HUST, China, in 2023, respectively. His current research interests include image/video processing, computer vision.

This paper proposes the first in-depth study of Transformer Networks (TF) and the Bidirectional Transformers (BERT) for the forecasting of the individual motion of people, without bells and whistles. We conduct an exhaustive evaluation of the input/output representations, problem formulations and sequence modelling, including a novel analysis of their capability to predict multi-modal futures. Out of comparative evaluation on the ETH+UCY benchmark, both TF and BERT are top performers in predicting individual motions and remain within a narrow margin wrt more complex techniques, including both social interactions and scene contexts. Source code will be released for all conducted experiments.

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Journals & BooksSearchRegisterSign inAccess through your institutionPurchase PDFSearch ScienceDirectArticle previewAbstractIntroductionSection snippetsReferences (38)Cited by (12)Recommended articles (6)Pattern RecognitionVolume 126, June 2023, 108552CSCNet: Contextual semantic consistency network for trajectory prediction in crowded spacesAuthor links open overlay panelBeihao Xia, Conghao Wong 1, Qinmu Peng, Wei Yuan, Xinge YouShow moreAdd to MendeleyShareCitedoi.org/10.1016/j.patcog.2023.108552Get rights and contentAbstractTrajectory prediction aims to predict the movement trend of the agents like pedestrians, bikers, vehicles. It is helpful to analyze and understand human activities in crowded spaces and widely applied in many areas such as surveillance video analysis and autonomous driving systems. Thanks to the success of deep learning, trajectory prediction has made significant progress. The current methods are dedicated to studying the agents’ future trajectories under the social interaction and the sceneries’ physical constraints. Moreover, how to deal with these factors still catches researchers’ attention. However, they ignore the Semantic Shift Phenomenon when modeling these interactions in various prediction sceneries. There exist several kinds of semantic deviations inner or between social and physical interactions, which we call the “Gap”. In this paper, we propose a Contextual Semantic Consistency Network (CSCNet) to predict agents’ future activities with powerful and efficient context constraints. We utilize a well-designed context-aware transfer to obtain the intermediate representations from the scene images and trajectories. Then we eliminate the differences between social and physical interactions by aligning activity semantics and scene semantics to cross the Gap. Experiments demonstrate that CSCNet performs better than most of the current methods quantitatively and qualitatively.

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The main contributions of this work are listed as follows:•We design a context-aware transfer to break the “Gap” within social and physical descriptions by aligning activity and scene semantics.

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We show some typical errors our model makes in Fig. 7. There are mainly two kinds of errors, including•Speed mismatch. Although the predicted direction is almost the same as its ground truth, the given speed does not match the reality in (a)–(c). CSCNet believes pedestrians may remain their previous speed if the interactive context has not changed drastically.

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