Intent Prediction of Pedestrians via Motion Trajectories Using Stacked Recurrent Neural Networks

Saleh, Khaled J.; Hossny, Mohammed; Nahavandi, Saeid

doi:10.1109/tiv.2018.2873901

Cited by 77 publications

(40 citation statements)

References 21 publications

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“…Intelligent autonomous systems will greatly depend on replication of the senses that we, humans, use to cooperate with others and learn in an adaptive manner [26,27]. Computer vision [28], combined with deep learning [29], reinforcement learning, and GPU-based computation [30], has shown great promise in replicating primitive vision and sensory capabilities. However, for Industry 5.0 cobots, these capabilities must be improved significantly.…”

Section: Advances In Sensing Technologies and Machine Cognitionmentioning

confidence: 99%

Industry 5.0—A Human-Centric Solution

2019

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Staying at the top is getting tougher and more challenging due to the fast-growing and changing digital technologies and AI-based solutions. The world of technology, mass customization, and advanced manufacturing is experiencing a rapid transformation. Robots are becoming even more important as they can now be coupled with the human mind by means of brain–machine interface and advances in artificial intelligence. A strong necessity to increase productivity while not removing human workers from the manufacturing industry is imposing punishing challenges on the global economy. To counter these challenges, this article introduces the concept of Industry 5.0, where robots are intertwined with the human brain and work as collaborator instead of competitor. This article also outlines a number of key features and concerns that every manufacturer may have about Industry 5.0. In addition, it presents several developments achieved by researchers for use in Industry 5.0 applications and environments. Finally, the impact of Industry 5.0 on the manufacturing industry and overall economy is discussed from an economic and productivity point of view, where it is argued that Industry 5.0 will create more jobs than it will take away.

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Section: Advances In Sensing Technologies and Machine Cognitionmentioning

confidence: 99%

Industry 5.0—A Human-Centric Solution

2019

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“…Recurrent neural networks (RNNs) for sequence learning, and long short-term memory (LSTM) networks in particular, have recently become a widely popular modeling approach for predicting human (Alahi et al, 2016; Bartoli et al, 2018; Sadeghian et al, 2019; Saleh et al, 2018b; Sun et al, 2018; Varshneya and Srinivasaraghavan, 2017; Vemula et al, 2018), vehicle (Altché and de La Fortelle, 2017; Ding et al, 2019; Kim et al, 2017; Park et al, 2018), and cyclist (Pool et al, 2019) motion. Alahi et al (2016) was the first to propose a Social-LSTM model to predict joint trajectories in continuous spaces.…”

Section: Pattern-based Approachesmentioning

confidence: 99%

Human motion trajectory prediction: a survey

Rudenko

Palmieri

Herman³

et al. 2020

The International Journal of Robotics Research

567

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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“…The dataset comprises a collection of 68 pedestrian sequences with 4 different pedestrian behaviour types: crossing, stopping, starting to walk, and bending-in. Though the dataset seems relatively small, the LSTM has been shown to perform well in the path prediction application [11].…”

Section: Datasetmentioning

confidence: 99%

“…Many studies use the SLDS as a framework [6], [7], [8], and [9]. Recently, the recurrent neural network (RNN) has been shown to be a promising approach [10,11,12,13]. Owing to the significant advancement of the state-of-the-art in pedestrian detection [14,15,16,17], we assume that the trajectories of the pedestrians are known in this study.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning and Statistical Models for Time-Critical Pedestrian Behaviour Prediction

Dabrowski¹,

Villiers²,

Rahman³

et al. 2019

Communications in Computer and Information Science

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The time it takes for a classifier to make an accurate prediction can be crucial in many behaviour recognition problems. For example, an autonomous vehicle should detect hazardous pedestrian behaviour early enough for it to take appropriate measures. In this context, we compare the switching linear dynamical system (SLDS) and a three-layered bi-directional long short-term memory (LSTM) neural network, which are applied to infer pedestrian behaviour from motion tracks. We show that, though the neural network model achieves an accuracy of 80%, it requires long sequences to achieve this (100 samples or more). The SLDS, has a lower accuracy of 74%, but it achieves this result with short sequences (10 samples). To our knowledge, such a comparison on sequence length has not been considered in the literature before. The results provide a key intuition of the suitability of the models in time-critical problems.

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Intent Prediction of Pedestrians via Motion Trajectories Using Stacked Recurrent Neural Networks

Cited by 77 publications

References 21 publications

Industry 5.0—A Human-Centric Solution

Industry 5.0—A Human-Centric Solution

Human motion trajectory prediction: a survey

Deep Learning and Statistical Models for Time-Critical Pedestrian Behaviour Prediction

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