Pedestrian Trajectory Prediction in Extremely Crowded Scenarios

Abstract

Pedestrian trajectory prediction under crowded circumstances is a challenging problem owing to human interaction and the complexity of the trajectory pattern. Various methods have been proposed for solving this problem, ranging from traditional Bayesian analysis to Social Force model and deep learning methods. However, most existing models heavily depend on specific scenarios because the trajectory model is constructed in absolute coordinates even though the motion trajectory as well as human interaction are in relative motion. In this study, a novel trajectory prediction model is proposed to capture the relative motion of pedestrians in extremely crowded scenarios. Trajectory sequences and human interaction are first represented with relative motion and then integrated to our model to predict pedestrians’ trajectories. The proposed model is based on Long Short Term Memory (LSTM) structure and consists of an encoder and a decoder which are trained by truncated back propagation. In addition, an anisotropic neighborhood setting is proposed instead of traditional neighborhood analysis. The proposed approach is validated using trajectory data acquired at an extremely crowded train station in Tokyo, Japan. The trajectory prediction experiments demonstrated that the proposed method outperforms existing methods and is stable for predictions of varying length even when the model is trained with a controlled short trajectory sequence.

Human motion trajectory prediction: a survey

“…Saleh et al predicted trajectories of pedestrians (Saleh et al, 2018b) and cyclists (Saleh et al, 2018a), adapting the LSTM architecture for the perspective of a moving vehicle. Numerous other implementations of the LSTM-based predictors offer various improvements, such as increased generalizability to new and crowded environments (Shi et al, 2019; Xue et al, 2019), considering the immediate (Zhang et al, 2019) or long-term (Xue et al, 2017) intention of the agents, augmenting the state of the person with the head pose (Hasan et al, 2018) or adding a better pooling mechanism with relative importance of each person in the vicinity of the target agent (Fernando et al, 2018; Pei et al, 2019; Xu et al, 2018). Huynh and Alaghband (2019) applied LSTM-based trajectory prediction in combination with local transition patterns, learned on the fly in a particular scene.…”

“…Muench and Gavrila (2019) decomposed the interactive planning problem into two policies with the corresponding Q-functions: one for prediction in static environment, and another for interaction prediction in an obstacle-free environment. Many deep learning methods consider interactions between participants: explicitly modeling interacting entities (Alahi et al, 2016; Amirian et al, 2019; Bartoli et al, 2018; Choi et al, 2019; Eiffert and Sukkarieh, 2019; Fernando et al, 2018, 2019; Gupta et al, 2018; Hasan et al, 2018; Huang et al, 2019; Ivanovic and Pavone, 2019; Kosaraju et al, 2019; Pei et al, 2019; Pfeiffer et al, 2018; Radwan et al, 2018; Rhinehart et al, 2019; Sadeghian et al, 2019; Saleh et al, 2019; Shi et al, 2019; Su et al, 2017; van der Heiden et al, 2019; Varshneya and Srinivasaraghavan, 2017; Vemula et al, 2018; Xu et al, 2018; Xue et al, 2018; Zhao et al, 2019), implicitly as a result of pixel-wise prediction (Walker et al, 2014), or by learning a joint motion policy (Lee et al, 2017; Ma et al, 2017; Shalev-Shwartz et al, 2016; Zhan et al, 2018). Many vehicle prediction methods consider interaction between traffic participants (e.g., Agamennoni et al, 2012; Altché and de La Fortelle, 2017; Bahram et al, 2016; Broadhurst et al, 2005; Chai et al, 2019; Cui et al, 2019; Dai et al, 2019; Deo and Trivedi, 2018; Ding et al, 2019; Djuric et al, 2018; Hong et al, 2019; Jain et al, 2019; Käfer et al, 2010; Kim et al, 2017…”

A Multi-Model Fusion-Based Indoor Positioning System Using Smartphone Inertial Measurement Unit Sensor Data

“…Several studies have therefore implemented hybrid algorithms to improve step detection accuracy. Some intelligent learning [7], [8] and hybrid [4], [9], [10] algorithms are presented and reviewed in the next paragraphs. In [10], a step detection algorithm that combines thresholding and windowed peak detection is presented.…”

“…Results from their step detection algorithm are used as input for the Kalman filter. Studies in [5] and [8] have explored machine learning techniques for step detection. Machine learning models such as long shortterm memory (LSTM) were used to differentiate locomotion modalities, phone placements and interfering activities such as calling and texting before step detection.…”

Long Short-Term Memory Neural Network Applied to Train Dynamic Model and Speed Prediction

“…Data-driven algorithms have been widely used in intelligent transportation systems [16,36,37]. In the area of traffic forecasting and trajectory prediction, data-driven approaches tend to outperform the parametric approaches.…”