483 research outputs found

    Wide baseline pose estimation from video with a density-based uncertainty model

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    International audienceRobust wide baseline pose estimation is an essential step in the deployment of smart camera networks. In this work, we highlight some current limitations of conventional strategies for relative pose estimation in difficult urban scenes. Then, we propose a solution which relies on an adaptive search of corresponding interest points in synchronized video streams which allows us to converge robustly toward a high-quality solution. The core idea of our algorithm is to build across the image space a nonstationary mapping of the local pose estimation uncertainty, based on the spatial distribution of interest points. Subsequently, the mapping guides the selection of new observations from the video stream in order to prioritize the coverage of areas of high uncertainty. With an additional step in the initial stage, the proposed algorithm may also be used for refining an existing pose estimation based on the video data; this mode allows for performing a data-driven self-calibration task for stereo rigs for which accuracy is critical, such as onboard medical or vehicular systems. We validate our method on three different datasets which cover typical scenarios in pose estimation. The results show a fast and robust convergence of the solution, with a significant improvement, compared to single image-based alternatives, of the RMSE of ground-truth matches, and of the maximum absolute error

    Latent Dependency Mining for Solving Regression Problems in Computer Vision

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    PhDRegression-based frameworks, learning the direct mapping between low-level imagery features and vector/scalar-formed continuous labels, have been widely exploited in computer vision, e.g. in crowd counting, age estimation and human pose estimation. In the last decade, many efforts have been dedicated by researchers in computer vision for better regression fitting. Nevertheless, solving these computer vision problems with regression frameworks remained a formidable challenge due to 1) feature variation and 2) imbalance and sparse data. On one hand, large feature variation can be caused by the changes of extrinsic conditions (i.e. images are taken under different lighting condition and viewing angles) and also intrinsic conditions (e.g. different aging process of different persons in age estimation and inter-object occlusion in crowd density estimation). On the other hand, imbalanced and sparse data distributions can also have an important effect on regression performance. Apparently, these two challenges existing in regression learning are related in the sense that the feature inconsistency problem is compounded by sparse and imbalanced training data and vice versa, and they need be tackled jointly in modelling and explicitly in representation. This thesis firstly mines an intermediary feature representation consisting of concatenating spatially localised feature for sharing the information from neighbouring localised cells in the frames. This thesis secondly introduces the cumulative attribute concept constructed for learning a regression model by exploiting the latent cumulative dependent nature of label space in regression, in the application of facial age and crowd density estimation. The thesis thirdly demonstrates the effectiveness of a discriminative structured-output regression framework to learn the inherent latent correlation between each element of output variables in the application of 2D human upper body pose estimation. The effectiveness of the proposed regression frameworks for crowd counting, age estimation, and human pose estimation is validated with public benchmarks

    Modeling and estimation of pedestrian flows in train stations

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    This thesis addresses two modeling problems related to pedestrian flows in train stations, namely that of estimating pedestrian origin-destination demand in rail access facilities, and that of describing the propagation of pedestrians in walking facilities. For both problems, a mathematical framework is developed at the aggregate level, describing pedestrians in terms of groups with the same departure time, origin and destination. The proposed demand estimator is probabilistic and accounts for within-day dynamics as well as for natural fluctuations across days. It is inspired by estimation methodologies that are used in the context of vehicular traffic. Critically, the proposed methodology takes the train timetable and ridership data into account, significantly improving the accuracy of the estimates. Other information sources, such as link flows or sales data, can also be incorporated. To describe the propagation of pedestrians, walkable space is considered as a network of pedestrian streams that interact locally. Based on the continuum theory for pedestrian flow and the cell transmission model, a computationally efficient model is obtained that can be used under a wide range of traffic conditions. An optional extension allows considering anisotropic flow, where the walking speed depends on the walking direction. Such a formulation is advantageous in particular at high densities. Throughout the thesis, a case study of Lausanne railway station is considered. A detailed discussion of the usage and level-of-service of its rail access facilities is provided, underlining the performance and practical applicability of the proposed modeling framework. The contribution of the thesis is fourfold. First, it provides a dedicated estimation methodology for pedestrian OD demand in train stations. Second, it proposes a novel macroscopic network loading model for congested and multi-directional pedestrian flows. Third, it presents a detailed case study of a Swiss train station, for which a rich data set is collected. Finally, it applies the aforementioned modeling framework to that case study, and provides practical guidance for its use in the planning and dimensioning of rail access facilities. Beyond train stations, the developed modeling framework can be readily applied to various other pedestrian facilities, such as airports, shopping malls, stadiums or urban walking areas. For instance, it may be used to support the organization, planning and design of such facilities, to safely and efficiently manage pedestrian flows using real-time monitoring and control, or to assess and optimize the safety both during normal use and in case of emergency

    How social learning strategies boost or undermine decision making in groups

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    Social interactions resulting in emergent collective behaviour play a key role in almost all layers of society, from local, small-scale interactions, such as people crossing the street, to global, large-scale interactions, such as the spread of fake news on online platforms. In our digital and interconnected world, it is increasingly important to understand the emergence of beneficial or detrimental collective dynamics. The characteristics of such dynamics are expected to depend greatly on the nature of information individuals have personally acquired and how they learn from others. Yet, how the decision-making processes shape the resulting collective dynamics remains poorly understood. When do individuals seek more information from social sources? How do individuals reap the benefits when navigating in social environments, and when do they fail to do so? This dissertation aims to answer these questions extending established theories and frameworks from individual decision-making into the social realm. This approach allows for the operationalization of personal and social information in a theory-driven manner, thereby achieving a deeper understanding of the individual-level decision process. The first chapter provides a introductory overview of the interplay between personal information use, social learning strategies and collective dynamics, and introduces the key theories and models I will expand on in this dissertation. In Chapter 2, inspired by Brunswick's lens model, I investigate how individuals form beliefs about the meaning of ecological structures (i.e., cues). Here, participants had to categorize images based on multiple cues, the meaning of which had to be learned over trials. I showed that participants observing the same cues formed different beliefs about the cue meanings. This diversity in cue beliefs is, in turn, an important process governing the quality of social information. The greater this diversity, the more independent personal information is, and the stronger the potential for social information use. Participants, however, failed to realize the full potential of this diversity because they only changed their personal decisions if a large majority disagreed with them. Simulating different strategies of social information use, I show that this reliance on strongly agreeing majorities impedes individuals from benefiting from diversity. This chapter thus identifies diversity in cue beliefs as an important factor allowing individuals in groups to benefit from the wisdom of each other, while simultaneously highlighting the importance of the individuals' social learning strategies to exploit this diversity. Chapter 3 dives deeper into the social learning strategies individuals use. By carefully controlling the social information displayed to participants, the study in this chapter provides an in-depth analysis of social learning strategies. Participants were confronted with an estimation task. They first provided an independent estimate, after which they observed estimates of others. Using Bayesian modelling techniques, I show that the incorporation of others' opinions strongly depends on how consistent those opinions are with an individual's own opinion and the degree of agreement among others. Individuals also strongly differ in the social learning strategies they use. These results elucidate what aspects are conducive for people to change their minds and contribute to the understanding of how individuals’ social information use shapes opinion and information dynamics in our interconnected society. In Chapter 4, I embed individuals a in temporal dynamic system which allows the investigation of the use of information in interaction with the emergent collective dynamic. Here, my focus is on social interactions where multiple people make decisions sequentially and thereby are simultaneously emitters and receivers of social information. To shed light on the unfolding dynamic in such settings, I will introduce the social drift-diffusion model (DDM). The model allows the investigation of the cognitive processes underlying the integration of personal and social information dynamically over time, and the subsequent collective dynamic. Analysis of the data shows that correct information spreads when the participants’ confidence reflects accuracy and when more confident participants decide faster. Under these conditions, later-deciding participants are likely to adopt social information and thereby to amplify the correct signal provided by early-deciding participants. The social DDM successfully captures all the key dynamics observed in the social system, revealing the cognitive underpinnings of information cascades in social systems. The general principles of personal and social information use that emerge from Chapter 4 allow to investigate the optimal behaviour when deciding sequentially. In Chapter 5, I develop an agent-based version of the social DDM and embed it in evolutionary algorithms, allowing the identification of evolutionarily advantageous strategies. I show that the individuals' decision time should depend on the quality of information, with the most accurate individuals deciding first. For all later.deciding individuals it is evolutionary advantageous to imitate the (often accurate) first decision. When introducing asymmetric error costs, single individuals should develop response biases to avoid the more costly error. In groups, however, such response biases can have dramatic consequences, as these biases are likely to be amplified in the group. As a result, individuals in large groups should use much weaker response biases to benefit from social information. I conclude that individuals facing asymmetric error costs in social environments need to carefully trade off the expressed response bias and sensitivity to social information to avoid the more costly error but simultaneously benefit from the collective. Overall, this thesis deepens our understanding of social dynamics by accounting for individual-level decision-making processes across various choice problems. I show that the behaviour of individuals in social environments can significantly differ depending on the personal information individuals possess and the strategies individuals use. Furthermore, I highlight the importance of accounting for such differences to predict the emergence of beneficial or detrimental dynamics in social environments
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