Behavior estimation for a complete framework for human motion prediction in crowded environments

Ferrer, Gonzalo; Sanfeliu, Alberto

doi:10.1109/icra.2014.6907734

Cited by 52 publications

(43 citation statements)

References 13 publications

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“…This is because the addition of a penalty breaks Fig. 4: Norm inducing reward function (depiction of (10)- (12)). The red agent is penalized if there is another agent in the blue, green or gray shaded regions, corresponding to overtaking, passing and crossing, respectively.…”

Section: A Inducing Social Normsmentioning

confidence: 99%

Socially aware motion planning with deep reinforcement learning

Chen

Everett

Liu

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

611

337

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Abstract-For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules (e.g., passing on the right). However, while instinctive to humans, socially compliant navigation is still difficult to quantify due to the stochasticity in people's behaviors. Existing works are mostly focused on using feature-matching techniques to describe and imitate human paths, but often do not generalize well since the feature values can vary from person to person, and even run to run. This work notes that while it is challenging to directly specify the details of what to do (precise mechanisms of human navigation), it is straightforward to specify what not to do (violations of social norms). Specifically, using deep reinforcement learning, this work develops a time-efficient navigation policy that respects common social norms. The proposed method is shown to enable fully autonomous navigation of a robotic vehicle moving at human walking speed in an environment with many pedestrians.

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Section: A Inducing Social Normsmentioning

confidence: 99%

Socially aware motion planning with deep reinforcement learning

Chen

Everett

Liu

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

611

337

View full text Add to dashboard Cite

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“…During experimentation in genuine situations, such module observed high fluctuation in human movement and it filled in as the inspiration to build up the present work. Given the high fluctuation of human conduct, it is hard to precisely anticipate human movement utilizing a similar arrangement of parameters, since high fluctuation of human conduct shows up a reasonable distinction amongst expectations and perceptions [40].…”

Section: Observation On Methods On Anomaly Detectionmentioning

confidence: 99%

Anomaly Detection in Crowded Scene by Pedestrians Behaviour Extraction using Long Short Term Method: A Comprehensive Study

Dey¹,

Mohammad²,

Ahmed³

et al. 2019

IJEME

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With the expansion of worldwide security concerns and a consistently expanding requirement for successful checking of open places, i.e. air terminals, railroad stations, shopping centres, crowded sports fields, army bases or smart healthcare facilities such as daily activity monitoring and fall detection in old people's homes is increasing very rapidly. The visual occlusions and ambiguities in crowded scenes, usage of suitable method and in addition the perplexing practices and scene semantics make the investigation a challenging task. This research demonstrates comprehensive and critical analysis of crowd scene involves in object detection, tracking, feature extraction and learning from visual surveillance which helps to recognize behavioural pattern. This research refers scene understanding as scene layout, i.e. finding streets, structures, sidewalks , vehicles turning, person on foot intersection and scene status such as crowd congestion, split, merge etc. The significance of the proposed comprehensive review to create crowd administration procedures and help the development of the group or people, to maintain a strategic distance from the group calamities and guarantee general society security. Based on the observation of previous research in three aspects, i.e. review based on methods, frameworks and critical existing results analysis, this research propose a framework for anomaly detection in crowded scene using LSTM (long Short-Term Method). Proposed comprehensive review is expected to contribute significantly for the investigation of behavior pattern analysis in computer vision research domains.

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“…Cues of the target agent include: 1.1. motion state such as position and possibly velocity (e.g., Bennewitz et al, 2005; Bera et al, 2016; Elfring et al, 2014; Ferrer and Sanfeliu, 2014; Karasev et al, 2016; Kitani et al, 2012; Kooij et al, 2019; Kucner et al, 2017; Kuderer et al, 2012; Pellegrini et al, 2009; Trautman and Krause, 2010; Ziebart et al, 2009); 1.2. articulated pose such as head orientation (e.g., Hasan et al, 2018; Kooij et al, 2019, 2014; Roth et al, 2016; Unhelkar et al, 2015) or fullbody pose (Mínguez et al, 2018; Quintero et al, 2014); 1.3.…”

Section: Taxonomymentioning

confidence: 99%

Human motion trajectory prediction: a survey

Rudenko

Palmieri

Herman³

et al. 2020

The International Journal of Robotics Research

607

306

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With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand, and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots, and advanced surveillance systems. This article provides a survey of human motion trajectory prediction. We review, analyze, and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.

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Behavior estimation for a complete framework for human motion prediction in crowded environments

Cited by 52 publications

References 13 publications

Socially aware motion planning with deep reinforcement learning

Socially aware motion planning with deep reinforcement learning

Anomaly Detection in Crowded Scene by Pedestrians Behaviour Extraction using Long Short Term Method: A Comprehensive Study

Human motion trajectory prediction: a survey

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