Pedestrian Trajectory Prediction Based on Deep Convolutional LSTM Network

Song, Xiao; Chen, Kai; Li, Xu; Sun, Jun; Hou, Baocun; Cui, Yong; Zhang, Baochang; Xiong, Gang; Wang, Zilie

doi:10.1109/tits.2020.2981118

Cited by 104 publications

(50 citation statements)

References 43 publications

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“…These components serve to model the more complex interactions required to define intention. In [54], instead of refining feature candidates using additional mechanisms, the authors choose to change the representation of the respective features. Specifically, LSTM layers are reconfigured and repurposed to handle multidimensional hidden states as opposed to the 1D vectors used traditionally.…”

Section: Lstm-based Methodsmentioning

confidence: 99%

A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving

2021

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This paper provides a literature review of some of the most important concepts, techniques, and methodologies used within autonomous car systems. Specifically, we focus on two aspects extensively explored in the related literature: tracking, i.e., identifying pedestrians, cars or obstacles from images, observations or sensor data, and prediction, i.e., anticipating the future trajectories and motion of other vehicles in order to facilitate navigating through various traffic conditions. Approaches based on deep neural networks and others, especially stochastic techniques, are reported.

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Section: Lstm-based Methodsmentioning

confidence: 99%

A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving

2021

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“…In an artificial neural network-based pedestrian movement model, the above information is taken as the input, which can be adjusted according to different environments. In previous works, trajectory position [15] and speed in the horizontal and vertical directions [13,20] of a pedestrian at the next time step are usually chosen as the outputs. In the study of Zhao et al [21], two submodels were developed to learn the magnitude and direction of pedestrian movement velocity, respectively.…”

Section: Development Of Ann-based Pedestrian Movement Behavior Modelmentioning

confidence: 99%

“…Specifically, distances along the X and Y axes between the subject pedestrian and neighboring pedestrians within his perception area are used to represent the influence of neighboring pedestrians in this submodel, as shown in Figure 2. Different from existing studies that a fixed number of nearest neighboring pedestrians, such as 5 or 7 were used in the inputs of the neural network model [11,20]; the number of input parameter in this submodel is not fixed. It is determined by the number of neighbors and the number of obstacles in the subject pedestrian's perception area at each time step.…”

Section: Velocity Displacement Submodel (Vdsm)mentioning

confidence: 99%

“…While the pedestrian moves down the corridor from right to left, θ t i is the angle rotating from vector (0, 1) to direction vector v →t−Δt i , as shown in Figure 4. To quantify the spatial distribution of the neighboring pedestrians, square grids were usually used in previous studies [20,21]. In this submodel, we adopt a radial-based method to divide a pedestrian's perception area.…”

Section: Velocity Direction Angle Submodel (Vdasm)mentioning

confidence: 99%

“…By learning realistic human knowledge, the simulation results exhibited great accuracy and realistic pedestrian behavior. In a convolutional LSTM model developed by Song et al [20], a multichannel tensor was used to represent spatial information about crowds, including scenario map, neighboring pedestrians, fixed obstacles, relative position in the crowd, and destination. e network is deepened and trained to predict spatiotemporal trajectory sequences.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data-Driven Simulation of Pedestrian Movement with Artificial Neural Network

Wang

Rong

Fan

et al. 2021

Journal of Advanced Transportation

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To predict pedestrian movement is of vital importance in a wide range of applications. Recently, data-driven models are receiving increasing attention in pedestrian dynamics studies, demonstrating a great potential in enhancing simulation performance. This paper presents a pedestrian movement simulation model based on the artificial neural network, in which two submodels are, respectively, used to predict velocity displacement and velocity direction angle at each time step. Destination information, the pedestrian’s historical movement information, neighboring pedestrians, and environmental obstacles within a semicircular-shaped perception area are used as inputs to learn pedestrian movement behavioral rules. In the velocity direction angle submodel, a novel division method on pedestrian’s perception area is adopted. Specifically, perception radius is divided into several bands, and perception angle range is divided into a number of sectors, establishing a weighted spatial matrix to represent varied influences of neighboring pedestrians and obstacles. Experiments on two typical scenarios, the unidirectional flow and bidirectional flow in a long straight corridor, were conducted to obtain pedestrian movement datasets. Then, a series of simulation cases were conducted to investigate the proper values for critical parameters, including perception radius, perception angle division, weights of the spatial matrix, and historical movement adoption. In comparison of pedestrian trajectory between simulation results and real data, the mean trajectory error (MTE) and mean destination error (MDE) are, respectively, 0.114 m and 0.171 m in the unidirectional flow scenario, which are, respectively, 0.204 m and 0.362 m in the bidirectional flow scenario. In addition, the fundamental diagram representing density-velocity and density-flow relationships in simulation results agree well with that in real data. The results demonstrate great capacity and credibility of the presented model in simulating pedestrian movement in real applications.

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Visual crowd analysis: Open research problems

Khan,

Menouar,

Hamila

2023

AI Magazine

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Over the last decade, there has been a remarkable surge in interest in automated crowd monitoring within the computer vision community. Modern deep‐learning approaches have made it possible to develop fully automated vision‐based crowd‐monitoring applications. However, despite the magnitude of the issue at hand, the significant technological advancements, and the consistent interest of the research community, there are still numerous challenges that need to be overcome. In this article, we delve into six major areas of visual crowd analysis, emphasizing the key developments in each of these areas. We outline the crucial unresolved issues that must be tackled in future works, in order to ensure that the field of automated crowd monitoring continues to progress and thrive. Several surveys related to this topic have been conducted in the past. Nonetheless, this article thoroughly examines and presents a more intuitive categorization of works, while also depicting the latest breakthroughs within the field, incorporating more recent studies carried out within the last few years in a concise manner. By carefully choosing prominent works with significant contributions in terms of novelty or performance gains, this paper presents a more comprehensive exposition of advancements in the current state‐of‐the‐art.

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Pedestrian Trajectory Prediction Based on Deep Convolutional LSTM Network

Cited by 104 publications

References 43 publications

A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving

A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving

Data-Driven Simulation of Pedestrian Movement with Artificial Neural Network

Visual crowd analysis: Open research problems

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