Search Results for author:
Found 9 papers, 8 papers with code
Efficient Multi-Resolution Fusion for Remote Sensing Data with Label Uncertainty
Previously, we developed a Multiple Instance Multi-Resolution Fusion (MIMRF) framework that addresses label uncertainty for fusion, but it can be slow to train due to the large search space for the fuzzy measures used to integrate sensor data sources.
BiPOCO: Bi-Directional Trajectory Prediction with Pose Constraints for Pedestrian Anomaly Detection
We present BiPOCO, a Bi-directional trajectory predictor with POse COnstraints, for detecting anomalous activities of pedestrians in videos.
Ranked #6 on
Video Anomaly Detection
on HR-Avenue
Leveraging Trajectory Prediction for Pedestrian Video Anomaly Detection
Video anomaly detection is a core problem in vision.
Coupling Intent and Action for Pedestrian Crossing Behavior Prediction
In this work, we follow the neuroscience and psychological literature to define pedestrian crossing behavior as a combination of an unobserved inner will (a probabilistic representation of binary intent of crossing vs. not crossing) and a set of multi-class actions (e. g., walking, standing, etc.).
BiTraP: Bi-directional Pedestrian Trajectory Prediction with Multi-modal Goal Estimation
BiTraP estimates the goal (end-point) of trajectories and introduces a novel bi-directional decoder to improve longer-term trajectory prediction accuracy.
Ranked #2 on
Trajectory Prediction
on JAAD
Stochastic Sampling Simulation for Pedestrian Trajectory Prediction
Our work demonstrates the effectiveness and potential of using simulation as a substitution for human annotation efforts to train high-performing prediction algorithms such as the DNNs.
Robotics
Bio-LSTM: A Biomechanically Inspired Recurrent Neural Network for 3D Pedestrian Pose and Gait Prediction
In applications such as autonomous driving, it is important to understand, infer, and anticipate the intention and future behavior of pedestrians.
Multi-Resolution Multi-Modal Sensor Fusion For Remote Sensing Data With Label Uncertainty
It is valuable to fuse outputs from multiple sensors to boost overall performance.
Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications
In classifier (or regression) fusion the aim is to combine the outputs of several algorithms to boost overall performance.
