Semi-supervised Elastic net for pedestrian counting

Pedestrian counting plays an important role in public safety and intelligent transportation. Most pedestrian counting algorithms based on supervised learning require much labeling work and rarely exploit the topological information of unlabelled data in a video. In this paper, we propose a Semi-Supervised Elastic Net (SSEN) regression method by utilizing sequential information between unlabelled samples and their temporally neighboring samples as a regularization term. Compared with a state-of-the-art algorithm, extensive experiments indicate that our algorithm can not only select sparse representative features from the original feature space without losing their interpretability, but also attain superior prediction performance with only very few labelled frames.     

基于计算机视觉的人流量双向统计

提出了一种采用视频监控系统对人行通道口进行双向人流量计数的方法.首先建立发色模型与头部形状模型,采用形态学运算提取人的头部目标,然后跟踪目标建立人头目标移动链,依据目标链位置信息判别行人的进出方向,最后设置感兴趣的检测区域,并对通过该检测区域的行人计数.实验结果表明,该方法能实时有效地统计通道口处双向人流量.     

Directional guidance from audible pedestrian signals for street crossing

Typical audible pedestrian signals indicate when the pedestrian walk interval is in effect but provide little, or even misleading information for directional alignment. In three experiments, blind and blindfolded sighted adults crossed a simulated crossing with recorded traffic noise to approximate street sounds. This was done to investigate how characteristics of signal presentation affected usefulness of the auditory signal for guiding crossing behaviour. Crossing was more accurate when signals came only from the far end of the crossing rather than the typical practice of presenting signals simultaneously from both ends. Alternating the signal between ends of the crossing was not helpful. Also, the customary practice of signalling two parallel crossings at the same time drew participants somewhat toward the opposite crossing. Providing a locator tone at the end of the crossing during the pedestrian clearance interval improved crossing accuracy. These findings provide a basis for designing audible pedestrian signals to enhance directional guidance. The principal findings were the same for blind and sighted participants and applied across a range of specific signals (e.g. chirps, clicks, voices).     

Directional guidance from audible pedestrian signals for street crossing

Typical audible pedestrian signals indicate when the pedestrian walk interval is in effect but provide little, or even misleading information for directional alignment. In three experiments, blind and blindfolded sighted adults crossed a simulated crossing with recorded traffic noise to approximate street sounds. This was done to investigate how characteristics of signal presentation affected usefulness of the auditory signal for guiding crossing behaviour. Crossing was more accurate when signals came only from the far end of the crossing rather than the typical practice of presenting signals simultaneously from both ends. Alternating the signal between ends of the crossing was not helpful. Also, the customary practice of signalling two parallel crossings at the same time drew participants somewhat toward the opposite crossing. Providing a locator tone at the end of the crossing during the pedestrian clearance interval improved crossing accuracy. These findings provide a basis for designing audible pedestrian signals to enhance directional guidance. The principal findings were the same for blind and sighted participants and applied across a range of specific signals (e.g. chirps, clicks, voices).     

可满足问题中的模型计数

模型计数问题是指计算给定问题的解的个数,这是一类比决策更困难的问题,也是人工智能领域研究的一个热点问题.对模型计数问题的研究不仅可以提高算法的求解效率,更能促进对问题困难本质的了解.以可满足问题（命题可满足（SAT）和约束可满足问题（CSP））为例,从精确算法和近似求解两方面综述了模型计数问题的研究现状,重点介绍了相关概念以及各个算法之间的优缺点,并提出了有待解决的开放性问题,对模型计数问题的研究予以了总结和展望.     

行人运动建模技术综述

随着计算机数值模拟技术的迅速发展,行人运动仿真技术开始成为人群疏散仿真、城市规划和计算机疏散软件开发方面的研究热点。介绍了对行人建模仿真的初步研究,重点介绍了基于行人行为的微观行人运动建模方法,并对各个模型进行了比较分析和研究展望。     

高斯白噪声对神经元映射模型动力学的影响*

研究了噪声引发的神经元相干共振现象。基于神经元二维映射模型,用高斯白噪声模拟生物神经系统中的环境噪声,通过数字仿真研究了噪声对神经元动力学特性的影响。研究发现,噪声可以提高静态神经元的可兴奋性,诱导其产生动作电位,当噪声达到某一适中强度时,动作电位的有序性可以达到最佳,产生相干共振现象。研究了系统参数的选取对神经元共振特性的影响,发现控制参数越接近分岔点,相干共振性越好。结果表明合适强度的噪声可以促进神经元的信息传递。     

A single-shot model for traffic-related pedestrian detection

Pattern Analysis and Applications - Traffic-related pedestrian detection is important for advanced driving-assistant systems and autonomous driving. In addition to pedestrian detection,...     

Probabilistic symbolic model checking with PRISM: a hybrid approach

In this paper we present efficient symbolic techniques for probabilistic model checking. These have been implemented in PRISM, a tool for the analysis of probabilistic models such as discrete-time Markov chains, continuous-time Markov chains and Markov decision processes using specifications in the probabilistic temporal logics PCTL and CSL. Motivated by the success of model checkers such as SMV which use BDDs (binary decision diagrams), we have developed an implementation of PCTL and CSL model checking based on MTBDDs (multi-terminal BDDs) and BDDs. Existing work in this direction has been hindered by the generally poor performance of MTBDD-based numerical computation, which is often substantially slower than explicit methods using sparse matrices. The focus of this paper is a novel hybrid technique which combines aspects of symbolic and explicit approaches to overcome these performance problems. For typical examples, we achieve a dramatic improvement over the purely symbolic approach. In addition, thanks to the compact model representation using MTBDDs, we can verify systems an order of magnitude larger than with sparse matrices, while almost matching or even beating them for speed.     

An intelligence-based route choice model for pedestrian flow in a transportation station

This study proposes a method that uses an artificial neural network (ANN) to mimic human decision-making about route choice in a crowded transportation station. Although ANN models have been developed rapidly and widely adopted in various fields in the last three decades, their application to predict human decision-making in pedestrian flows is limited, because the video clip technology used to collect pedestrian movement data in crowded conditions is still primitive. Data collection must be carried out manually or semi-manually, which requires extensive resources and is time consuming. This study adopts a semi-manual approach to extract data from video clips to capture the route choice behaviour of travellers, and then applies an ANN to mimic such decision-making. A prediction accuracy of 86% (ANN model with ensemble approach) is achieved, which demonstrates the feasibility of applying the ANN approach to decision-making in pedestrian flows.     

基于注意力机制的行人轨迹预测生成模型

针对长短期记忆网络（LSTM）在行人轨迹预测问题中孤立考虑单个行人,且无法进行多种可能性预测的问题,提出基于注意力机制的行人轨迹预测生成模型（AttenGAN）,来对行人交互模式进行建模和概率性地对多种合理可能性进行预测。AttenGAN包括一个生成器和一个判别器,生成器根据行人过去的轨迹概率性地对未来进行多种可能性预测,判别器用来判断一个轨迹是真实的还是由生成器伪造生成的,进而促进生成器生成符合社会规范的预测轨迹。生成器由一个编码器和一个解码器组成,在每一个时刻,编码器的LSTM综合注意力机制给出的其他行人的状态,将当前行人个体的信息编码为隐含状态。预测时,首先用编码器LSTM的隐含状态和一个高斯噪声连接来对解码器LSTM的隐含状态初始化,解码器LSTM将其解码为对未来的轨迹预测。在ETH和UCY数据集上的实验结果表明,AttenGAN模型不仅能够给出符合社会规范的多种合理的轨迹预测,并且在预测精度上相比传统的线性模型（Linear）、LSTM模型、社会长短期记忆网络模型（S-LSTM）和社会对抗网络（S-GAN）模型有所提高,尤其在行人交互密集的场景下具有较高的精度性能。对生成器多次采样得到的预测轨迹的可视化结果表明,所提模型具有综合行人交互模式,对未来进行联合性、多种可能性预测的能力。     

Effective pedestrian detection using deformable part model based on human model

Recently, pedestrian detection systems have become an important technology in the development of the advanced driver assistance system (ADAS) for the autonomous car. The histogram of oriented gradients (HOG) is currently the most basic algorithm for detecting pedestrians, but it treats the entire body of the pedestrian as one single feature. In other words, if the entire body of the pedestrian is not visible, the detection rate under HOG decreases markedly. To solve this problem, we propose a detection system using a deformable part model (DPM) that divides the pedestrian data into two parts using a latent support vector machine (SVM)-based machine-learning technique. Experimental results show that our approach achieves better performance in a detection system than the existing method. In practice, there are many occlusions in the environment in front of the vehicle. For example, the surrounding transport facilities, such as a car or another obstacle, can occlude a pedestrian. These occlusions can increase the false detection rate and cause difficulties during the detection process. Our proposed method uses a different approach and can easily be applied in real-world scenarios, regardless of occlusions.

     

Aligning music audio with symbolic scores using a hybrid graphical model

We present a new method for establishing an alignment between a polyphonic musical score and a corresponding sampled audio performance. The method uses a graphical model containing both latent discrete variables, corresponding to score position, as well as a latent continuous tempo process. We use a simple data model based only on the pitch content of the audio signal. The data interpretation is defined to be the most likely configuration of the hidden variables, given the data, and we develop computational methodology to identify or approximate this configuration using a variant of dynamic programming involving parametrically represented continuous variables. Experiments are presented on a 55-minute hand-marked orchestral test set. This work supported by NSF grants IIS-0113496 and IIS-0534694. Editor: Gerhard Widmer     

Modified diffusion model with multiple products using a hybrid GA approach

As technology advances, the speed in which new products are developed also increases. Due to such increases, product forecasting has become much more vital for a company. The Bass diffusion model is a demand-forecast model that explores the phases of a product’s life cycle that have been successful in the diffusion of forecasting innovation in new products. Recognizing the need for an efficient parameter estimation method for multi-product forecasting, we have conducted research using the hybrid genetic algorithm (HGA). The research conducted will provide an alternate approach to explore the forecasting capability of the diffusion models without having as many limitations as the original method. We used both published data and LCD-monitor global sales data to test and verify our method. Results show that the proposed model using a hybrid GA approach can improve the forecasting efficiency.     

行人交通微观动态元胞自动机仿真模型研究

在分析行人交通特性后,以元胞自动机（CA）和规则描述为理论基础,提出基于CA的行人仿真模型,以此建立相对于其他交通对象的行人避让模型。该方法以模型与现实世界的高相似性有效克服了精确数学模型在仿真行人交通时的难题。结合行人CA模型和行人避让模型开发了混合交通流仿真试验原型系统。仿真结果表明,该方法有效地模拟了现实交通状况,在城市交通规划、交通需求管理和交通控制等领域有着广泛的应用前景。     

LU-ReNet植物叶片分割与计数模型

为完成一种植物叶片分割与计数模型LU-ReNet,利用Xception module和CoordConv改进模型编码器,通过改进的ReNet模型并参照Unet模型结构将CNN和RNN结合在一起.在CVPPP的A1、A2、A3基准数据集上,LU-ReNet模型取得了0.95的平均F1-score、95.54％的平均前景-...     

Dynamic Kernel CNN-LR model for people counting

Applied Intelligence - People Counting in images is a worthwhile task as it is widely used for public safety, emergency people planning, intelligent crowd flow, and countless other reasons....     

On probabilistic inference by weighted model counting

A recent and effective approach to probabilistic inference calls for reducing the problem to one of weighted model counting (WMC) on a propositional knowledge base. Specifically, the approach calls for encoding the probabilistic model, typically a Bayesian network, as a propositional knowledge base in conjunctive normal form (CNF) with weights associated to each model according to the network parameters. Given this CNF, computing the probability of some evidence becomes a matter of summing the weights of all CNF models consistent with the evidence. A number of variations on this approach have appeared in the literature recently, that vary across three orthogonal dimensions. The first dimension concerns the specific encoding used to convert a Bayesian network into a CNF. The second dimensions relates to whether weighted model counting is performed using a search algorithm on the CNF, or by compiling the CNF into a structure that renders WMC a polytime operation in the size of the compiled structure. The third dimension deals with the specific properties of network parameters (local structure) which are captured in the CNF encoding. In this paper, we discuss recent work in this area across the above three dimensions, and demonstrate empirically its practical importance in significantly expanding the reach of exact probabilistic inference. We restrict our discussion to exact inference and model counting, even though other proposals have been extended for approximate inference and approximate model counting.