Query: Pedestrian psychology

title: Be Responsible?
abstract: The bystander effect reveals that people are less likely to help a person in need when others are present. We examined the impact of priming the concept of responsibility on the bystander effect in a field study. Lone pedestrians (N 259) were randomly assigned to a two (Bystanders: none and three nonresponsive bystanders) by two (Shirt: blank shirt and shirt with "Be Responsible" written on the front) design. A researcher dropped eight pens approximately 15 ft from a lone pedestrian, while wearing one of the two shirts in the presence/absence of bystanders (confederates) The bystander effect was found: Pedestrians helped pick up pens more frequently in the no bystanders condition (59.05% helped) compared to the nonresponsive bystanders condition (41.67% helped) The responsibility prime tended to boost helping rates, but it did not significantly increase helping rates either as a main effect or as part of an interaction term. The bystander effect was replicated in a field setting, but priming the concept of responsibility did not appear to reduce it.

title: Online Multiple Pedestrian Tracking using Deep Temporal Appearance Matching Association
abstract: In online multiple pedestrian tracking, it is of great importance to model appearance and geometric similarity between existing tracks and targets appeared in a new frame. The appearance model contains discriminative information with higher dimension compared to the geometric model. Thanks to the recent success of deep learning based methods, handling of high dimensional appearance information becomes possible. Among many deep networks, the Siamese network with triplet loss is popularly adopted as an appearance feature extractor. Since the Siamese network can extract features of each input independently, it is possible to update and maintain target specific features. However, it is not suitable for multi object settings that require comparison with other inputs. In this paper we propose a novel track appearance model based on joint inference network to address this issue. The proposed method enables comparison of two inputs to be used for adaptive appearance modeling. It contributes to disambiguating the process of target observation matching and consolidating the identity consistency. Diverse experimental results support effectiveness of our method. Our work has been awarded as a 3rd highest tracker on MOTChallenge19, held in CVPR2019.

title: PSC Net: Learning Part Spatial Co occurence for Occluded Pedestrian Detection
abstract: Detecting pedestrians, especially under heavy occlusions, is a challenging computer vision problem with numerous real world applications. This paper introduces a novel approach, termed as PSC Net, for occluded pedestrian detection. The proposed PSC Net contains a dedicated module that is designed to explicitly capture both inter and intra part co occurrence information of different pedestrian body parts through a Graph Convolutional Network (GCN) Both inter and intra part co occurrence information contribute towards improving the feature representation for handling varying level of occlusions, ranging from partial to severe occlusions. Our PSC Net exploits the topological structure of pedestrian and does not require part based annotations or additional visible bounding box (VBB) information to learn part spatial co occurrence. Comprehensive experiments are performed on two challenging datasets: CityPersons and Caltech datasets. The proposed PSC Net achieves state of the art detection performance on both. On the heavy occluded \textbf{HO} set of CityPerosns test set, our PSC Net obtains an absolute gain of 4.0\ in terms of log average miss rate over the state of the art with same backbone, input scale and without using additional VBB supervision. Further, PSC Net improves the state of the art from 37.9 to 34.8 in terms of log average miss rate on Caltech \textbf{HO} test set.

title: Pedestrian Models for Autonomous Driving Part II: High Level Models of Human Behavior
abstract: Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high streets. Unlike static obstacles, pedestrians are active agents with complex, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behavior as well as detecting and tracking them. This narrative review article is Part II of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low level image detection to high level psychological models, from the perspective of an AV designer. This self contained Part II covers the higher levels of this stack, consisting of models of pedestrian behavior, from prediction of individual pedestrians' likely destinations and paths, to game theoretic models of interactions between pedestrians and autonomous vehicles. This survey clearly shows that, although there are good models for optimal walking behavior, high level psychological and social modelling of pedestrian behavior still remains an open research question that requires many conceptual issues to be clarified. Early work has been done on descriptive and qualitative models of behavior, but much work is still needed to translate them into quantitative algorithms for practical AV control.

title: Agent based simulation of pedestrian dynamics for exposure time estimation in epidemic risk assessment
abstract: With the coronavirus disease 2019 (COVID 19) pandemic spreading across the world, protective measures for containing the virus are essential, especially as long as no vaccine or effective treatment is available. One important measure is the so called physical distancing or social distancing. In this paper, we propose an agent based numerical simulation of pedestrian dynamics in order to assess the behavior of pedestrians in public places in the context of contact transmission of infectious diseases like COVID 19, and to gather insights about exposure times and the overall effectiveness of distancing measures. To abide by the minimum distance of 1.5 m stipulated by the German government at an infection rate of 2% our simulation results suggest that a density of one person per 16m2 or below is sufficient. The results of this study give insight into how physical distancing as a protective measure can be carried out more efficiently to help reduce the spread of COVID 19.

title: Coupled Network for Robust Pedestrian Detection With Gated Multi Layer Feature Extraction and Deformable Occlusion Handling
abstract: Pedestrian detection methods have been significantly improved with the development of deep convolutional neural networks. Nevertheless, detecting ismall scaled pedestrians and occluded pedestrians remains a challenging problem. In this paper, we propose a pedestrian detection method with a couple network to simultaneously address these two issues. One of the sub networks, the gated multi layer feature extraction sub network, aims to adaptively generate discriminative features for pedestrian candidates in order to robustly detect pedestrians with large variations on scale. The second sub network targets on handling the occlusion problem of pedestrian detection by using deformable regional region of interest (RoI) pooling. We investigate two different gate units for the gated sub network, namely, the channel wise gate unit and the spatio wise gate unit, which can enhance the representation ability of the regional convolutional features among the channel dimensions or across the spatial domain, repetitively. Ablation studies have validated the effectiveness of both the proposed gated multi layer feature extraction sub network and the deformable occlusion handling sub network. With the coupled framework, our proposed pedestrian detector achieves promising results on both two pedestrian datasets, especially on detecting small or occluded pedestrians. On the CityPersons dataset, the proposed detector achieves the lowest missing rates (i.e. 40.78% and 34.60% on detecting small and occluded pedestrians, surpassing the second best comparison method by 6.0% and 5.87% respectively.

title: Pedestrian Trajectory Prediction Based on Deep Convolutional LSTM Network
abstract: Pedestrian trajectory prediction is vital for transportation systems. Generally we can divide pedestrian behavior modeling into two categories, i.e. knowledge driven and data driven. The former might bring expert bias, and it sometimes generates unrealistic pedestrian movement due to unnecessary repulsive forces. The latter approach is popular nowadays but most existing neural networks, including fully connected long short term memory (LSTM) networks, use a 1D vector to model their input and state. The shortcoming is that these works cannot learn spatial information about pedestrians, especially in a dense crowd. To tackle this, we propose to use tensors to represent essential environment features of pedestrians. Accordingly, a convolutional LSTM is designed and deepened to predict spatiotemporal trajectory sequences. As the tensor and convolution can learn better spatiotemporal interactions among pedestrians and environments, experimental results show that the proposed network can estimate more realistic trajectories for a dense crowd in evacuation and counterflow.

title: Modeling indoor level non pharmaceutical interventions during the COVID 19 pandemic: A pedestrian dynamics based microscopic simulation approach
abstract: Mathematical modeling of epidemic spreading has been widely adopted to estimate the threats of epidemic diseases (i.e. the COVID 19 pandemic) as well as to evaluate epidemic control interventions. The indoor place is considered to be a significant epidemic spreading risk origin, but existing widely used epidemic spreading models are usually limited for indoor places since the dynamic physical distance changes between people are ignored, and the empirical features of the essential and non essential travel are not differentiated. In this paper, we introduce a pedestrian based epidemic spreading model that is capable of modeling indoor transmission risks of diseases during people's social activities. Taking advantage of the before and after mobility data from the University of Maryland COVID 19 Impact Analysis Platform, it's found that people tend to spend more time in grocery stores once their travel frequencies are restricted to a low level. In other words, an increase in dwell time could balance the decrease in travel frequencies and satisfy people's demands. Based on the pedestrian based model and the empirical evidence, combined non pharmaceutical interventions from different operational levels are evaluated. Numerical simulations show that restrictions on people's travel frequency and open hours of indoor places may not be universally effective in reducing average infection risks for each pedestrian who visit the place. Entry limitations can be a widely effective alternative, whereas the decision maker needs to balance the decrease in risky contacts and the increase in queue length outside the place that may impede people from fulfilling their travel needs. The results show that a good coordination among the decision makers can contribute to the improvement of the performance of combined non pharmaceutical interventions, and it also benefits the short term and long term interventions in the future.

title: Benchmark for Evaluating Pedestrian Action Prediction
abstract: Pedestrian action prediction has been a topic of active research in recent years resulting in many new algorithmic solutions. However, measuring the overall progress towards solving this problem is difficult due to the lack of publicly available benchmarks and common training and evaluation procedures. To this end, we introduce a benchmark based on two public datasets for pedestrian behavior understanding. Using the proposed evaluation procedures, we rank a number of baseline and state of the art models and analyze their performance with respect to various properties of the data. Based on these findings we propose a new model for pedestrian crossing action prediction that uses attention mechanisms to effectively combine implicit and explicit features and demonstrate new state of the art results. The code for models and evaluation is available at https:/github.com/ykotseruba/PedestrianActionBenchmark.

title: Pedestrian Dead Reckoning With Wearable Sensors: A Systematic Review
abstract: Pedestrian Dead Reckoning (PDR) is the process of calculating one's current location by using the previously known position, and advancing that position over time using established or estimated speeds and trajectories (or alternatively stride lengths and directions) PDR plays an important role in modern life, including tracking locations of people and objects whenever GPS is not available. Self contained PDR systems do not require an infrastructure, thus they can be used for rapid deployment in situations such as search and rescue, disaster relief or medical emergencies. Wearable sensors are often applied in self contained PDR, but implementation varies in terms of the number, type and location of sensors used. Many algorithms are designed for PDR in order to reduce the error or drift of the final estimate, with various levels of success. There is a lack of comparison between these different methods and this systematic review of PDR for wearable devices provides a comprehensive overview that can inform further design optimizations. The aim of this article is to assess the quality of all available PDR literature with a focus on wearable sensors. It provides an outline of the state of the art in the field by comparing the accuracy of different sensor layouts and algorithms. Further directions of research are suggested based on these results. This study also highlights the need for more standardised and robust assessment protocols to capture real world tracking performance of PDR methods.