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To improve foresight and make correct judgment in advance, pedestrian trajectory prediction has a wide range of application values in autonomous driving, robot interaction, and safety monitoring. However, most of the existing methods only focus on the interaction of local pedestrians according to distance, ignoring the influence of far pedestrians; the range of network input (receptive field) is small. In this paper, an extended graph attention network (EGAT) is proposed to increase receptive field, which focuses not only on local pedestrians, but also on those who are far away, to further strengthen pedestrian interaction. In the temporal domain, TSG-LSTM (TS-LSTM and TG-LSTM) and P-LSTM are proposed based on LSTM to enhance information transmission by residual connection. Compared with state-of-the-art methods, the model EGAT achieves excellent performance on both ETH and UCY public datasets and generates more reliable trajectories., Competing Interests: The authors declare that they have no conflicts of interest regarding the publication of this paper., (Copyright © 2021 Wei Kong et al.)

Pedestrian tracking is a critical problem in the field of computer vision. Particle filters have been proven to be very useful in pedestrian tracking for nonlinear and non-Gaussian estimation problems. However, pedestrian tracking in complex environment is still facing many problems due to changes of pedestrian postures and scale, moving background, mutual occlusion, and presence of pedestrian. To surmount these difficulties, this paper presents tracking algorithm of multiple pedestrians based on particle filters in video sequences. The algorithm acquires confidence value of the object and the background through extracting a priori knowledge thus to achieve multipedestrian detection; it adopts color and texture features into particle filter to get better observation results and then automatically adjusts weight value of each feature according to current tracking environment. During the process of tracking, the algorithm processes severe occlusion condition to prevent drift and loss phenomena caused by object occlusion and associates detection results with particle state to propose discriminated method for object disappearance and emergence thus to achieve robust tracking of multiple pedestrians. Experimental verification and analysis in video sequences demonstrate that proposed algorithm improves the tracking performance and has better tracking results.

• We propose a spatio-temporal graph attention network to capture global information. • We design SA and TAM to enhance the association of spatio-temporal attention. • We present FUM and FSM for increasing the spatio-temporal receptive fields. • High computational efficiency helps to improve the reasoning. Most encoder-decoder structure based predictions models usually predict trajectory according to the position and historical movement of nearby pedestrians. Their input range (receptive field) is small. They often ignore some specific information such as the speed and direction of pedestrians' movement or the temporal attention. This leads to detailed pedestrian interaction that cannot be obtained. Therefore, we propose a novel spatio-temporal graph attention network (GAT) called GSTA. In the spatial domain, GSTA obtains complex interaction by spatial attention (SA) based on multi-feature fusion, and expands the receptive field through feature updating mechanism (FUM). In the temporal domain, we design temporal attention module (TAM) and feature selection module (FSM). TAM is used to discover the internal relationship of historical trajectory and solve the problem that temporal attention is averaged. FSM overcomes the adverse effect of small temporal perceptual range and reasonably controls the flow of feature information. Experimental results on 5 commonly used pedestrian trajectory datasets show that the prediction accuracy of our proposed model is further improved. [ABSTRACT FROM AUTHOR]