The intelligent vehicle community has devoted considerable efforts to model driver behavior, and in particular to detect and overcome driver distraction in an effort to reduce accidents caused by driver negligence. However, as the domain increasingly shifts towards autonomous and semi-autonomous solutions, the driver is no longer integral to the decision making process, indicating a need to refocus efforts elsewhere. To this end, we propose to study pedestrian distraction instead. In particular, we focus on detecting pedestrians who are engaged in secondary activities involving their cellphones and similar handheld multimedia devices from a purely vision-based standpoint. To achieve this objective, we propose a pipeline incorporating articulated human pose estimation, followed by a soft object label transfer from an ensemble of exemplar SVMs trained on the nearest neighbors in pose feature space. We additionally incorporate head gaze features and prior pose information to carry out cellphone related pedestrian activity recognition. Finally, we offer a method to reliably track the articulated pose of a pedestrian through a sequence of images using a particle filter with a Gaussian Process Dynamical Model (GPDM), which can then be used to estimate sequentially varying activity scores at a very low computational cost. The entire framework is fast (especially for sequential data) and accurate, and easily extensible to include other secondary activities and sources of distraction.
The terms action and activity have been used quite loosely in the context of pedestrians. In most cases, these terms allude to the different stages in the trajectory of a pedestrian @cite_25 @cite_8 , e.g. walking, waiting, crossing etc. This notion of activity has also been extended to groups of people, where portions of a crowd are assigned a common activity based on context and collective behavior @cite_13 . In this study, we use the term activity to refer to the activity of a pedestrian being performed in addition to walking crossing.
@cite_13 @cite_25 @cite_8
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In this paper we present a new framework for pedestrian action categorization. Our method enables the classification of actions whose semantic can be only analyzed by looking at the collective behavior of pedestrians in the scene. Examples of these actions are waiting by a street intersection versus standing in a queue. To that end, we exploit the spatial distribution of pedestrians in the scene as well as their pose and motion for achieving robust action classification. Our proposed solution employs extended Kalman filtering for tracking of detected pedestrians in 2D 1 2 scene coordinates as well as camera parameter and horizon estimation for tracker filtering and stabilization. We present a local spatio-temporal descriptor effective in capturing the spatial distribution of pedestrians over time as well as their pose. This descriptor captures pedestrian activity while requiring no high level scene understanding. Our work is tested against highly challenging real world pedestrian video sequences captured by low resolution hand held cameras. Experimental results on a 5-class action dataset indicate that our solution: i) is effective in classifying collective pedestrian activities; ii) is tolerant to challenging real world conditions such as variation in illumination, scale, viewpoint as well as partial occlusion and background motion; iii) outperforms state-of-the art action classification techniques. It is suggested that the motion of pedestrians can be described as if they would be subject to social forces.'' These forces'' are not directly exerted by the pedestrians' personal environment, but they are a measure for the internal motivations of the individuals to perform certain actions (movements). The corresponding force concept is discussed in more detail and can also be applied to the description of other behaviors. In the presented model of pedestrian behavior several force terms are essential: first, a term describing the acceleration towards the desired velocity of motion; second, terms reflecting that a pedestrian keeps a certain distance from other pedestrians and borders; and third, a term modeling attractive effects. The resulting equations of motion of nonlinearly coupled Langevin equations. Computer simulations of crowds of interacting pedestrians show that the social force model is capable of describing the self-organization of several observed collective effects of pedestrian behavior very realistically. Starting with a short review of the available literature in the field of pedestrian and evacuation research, an overview is given over the observed collective phenomena in pedestrian crowds. This includes lane formation in corridors and oscillations at bottlenecks in normal situations, while different kinds of blocked states are produced in panic situations. By means of molecular-dynamic-like microsimulations based on a generalized force model of interactive pedestrian dynamics, the spatio-temporal patterns in pedestrian crowds are successfully reproduced and interpreted as self-organized phenomena. In contrast to previous socio-psychological approaches, this allows a physical understanding of the observations. Despite the significantly different phenomena occuring in normal and panic situations, the main effects can be described by a unified model containing only well interpretable and plausible terms. The transition between the “rational” normal behavior and the apparently “irrational” panic behavior is controlled by a single parameter, the “nervousness”, which influences fluctuation strengths, desired speeds, and the tendency of herding. Thereby, it causes paradoxial effects like “freezing by heating”, “faster is slower”, and the ignorance of available exits. Nevertheless, there are measures to improve pedestrian flows, both in normal and panic situations. For example, the suitable placement of columns can help, although they reduce the accessible space. Crowd behavior understanding is crucial yet challenging across a wide range of applications, since crowd behavior is inherently determined by a sequential decision-making process based on various factors, such as the pedestrians' own destinations, interaction with nearby pedestrians and anticipation of upcoming events. In this paper, we propose a novel framework of Social-Aware Generative Adversarial Imitation Learning (SA-GAIL) to mimic the underlying decision-making process of pedestrians in crowds. Specifically, we infer the latent factors of human decision-making process in an unsupervised manner by extending the Generative Adversarial Imitation Learning framework to anticipate future paths of pedestrians. Different factors of human decision making are disentangled with mutual information maximization, with the process modeled by collision avoidance regularization and Social-Aware LSTMs. Experimental results demonstrate the potential of our framework in disentangling the latent decision-making factors of pedestrians and stronger abilities in predicting future trajectories.