Journal Paper

BiTraP: Bi-directional Pedestrian Trajectory Prediction with Multi-modal Goal Estimation

Abstract Pedestrian trajectory prediction is an essential task in robotic applications such as autonomous driving and robot navigation. State-of-the-art trajectory predictors use a conditional variational autoencoder (CVAE) with recurrent neural networks (RNNs) to encode observed trajectories and decode multi-modal future trajectories. This process can suffer from accumulated errors over long prediction horizons (>=2 seconds). This …

Unsupervised Pedestrian Pose Prediction: A deep predictive coding network-based approach for autonomous vehicle perception

by Journal Paper No Comments

Abstract Pedestrian pose prediction is an important topic related closely to robotics and automation. Accurate predictions of human pose and motion can facilitate a more thorough understanding and analysis of human behavior, which benefits real-world applications, such as human-robot interaction, humanoid and bipedal robot design, and safe navigation of mobile robots and autonomous vehicles. This …

Pedestrian Planar LiDAR Pose (PPLP) Network for Oriented Pedestrian Detection Based on Planar LiDAR and Monocular Images

by Journal Paper No Comments

Abstract Pedestrian detection is an important task for human-robot interaction and autonomous driving applications. Most previous pedestrian detection methods rely on data collected from three-dimensional (3D) Light Detection and Ranging (LiDAR) sensors in addition to camera imagery, which can be expensive to deploy. In this letter, we propose a novel Pedestrian Planar LiDAR Pose Network …

Multiresolution Multimodal Sensor Fusion for Remote Sensing Data With Label Uncertainty

by Journal Paper No Comments

Abstract In remote sensing, each sensor can provide complementary or reinforcing information. It is valuable to fuse outputs from multiple sensors to boost overall performance. Previous supervised fusion methods often require accurate labels for each pixel in the training data. However, in many remote sensing applications, pixel-level labels are difficult or infeasible to obtain. In …

Bio-LSTM: A Biomechanically Inspired Recurrent Neural Network for 3D Pedestrian Pose and Gait Prediction

by Journal Paper No Comments

Abstract In applications such as autonomous driving, it is important to understand, infer, and anticipate the intention and future behavior of pedestrians. This ability allows vehicles to avoid collisions and improve ride safety and quality. This paper proposes a biomechanically inspired recurrent neural network (Bio-LSTM) that can predict the location and 3D articulated body pose …

Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications

by Journal Paper No Comments

Abstract In classifier (or regression) fusion, the aim is to combine the outputs of several algorithms to boost overall performance. Standard supervised fusion algorithms often require accurate and precise training labels. However, accurate labels may be difficult to obtain in many remote sensing applications. This paper proposes novel classification and regression fusion models that can …

Spatial and Spectral Unmixing Using the Beta Compositional Model

by Journal Paper No Comments

Abstract This paper introduces the beta compositional model (BCM) for hyperspectral unmixing and four algorithms for unmixing given the BCM. Hyperspectral unmixing estimates the proportion of each endmember at every pixel of a hyperspectral image. Under the BCM, each endmember is a random variable distributed according to a beta distribution. By using a beta distribution, …