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Found 5 papers, 4 papers with code
Predicting Pedestrian Crossing Intention with Feature Fusion and Spatio-Temporal Attention
This work addresses the above limitations by introducing a novel neural network architecture to fuse inherently different spatio-temporal features for pedestrian crossing intention prediction.
On the Generalizability of Motion Models for Road Users in Heterogeneous Shared Traffic Spaces
The results indicate that our model can realistically simulate a wide range of motion behaviors and interaction scenarios, and that adding different motion patterns of pedestrians into our model improves its performance.
Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion
The main strategy is to depend solely on the 2D vision for recognizing object class, as object shape does not change drastically with an increase in depth, and use pointcloud data for object localization in the 3D space for faraway objects.
A Vision-based Social Distancing and Critical Density Detection System for COVID-19
If a violation is detected, a non-intrusive audio-visual warning signal is emitted without targeting the individual who breached the social distancing measure.
Top-view Trajectories: A Pedestrian Dataset of Vehicle-Crowd Interaction from Controlled Experiments and Crowded Campus
The final trajectories of pedestrians and vehicles were refined by Kalman filters with linear point-mass model and nonlinear bicycle model, respectively, in which xy-velocity of pedestrians and longitudinal speed and orientation of vehicles were estimated.
