Robust Pedestrian Detection and Path Prediction using Improved YOLOv5

Context-Based Pedestrian Path Prediction

We present a novel Dynamic Bayesian Network for pedestrian path prediction in the intelligent vehicle domain. The model incorporates the pedestrian situational awareness, situation criticality and spatial layout of the environment as latent states on top of a Switching Linear Dynamical System (SLDS) to anticipate changes in the pedestrian dynamics. Using computer vision, situational awareness i...

Local Decorrelation For Improved Pedestrian Detection

Even with the advent of more sophisticated, data-hungry methods, boosted decision trees remain extraordinarily successful for fast rigid object detection, achieving top accuracy on numerous datasets. While effective, most boosted detectors use decision trees with orthogonal (single feature) splits, and the topology of the resulting decision boundary may not be well matched to the natural topolo...

Pedestrian Path Prediction with Recursive Bayesian Filters: A Comparative Study

In the context of intelligent vehicles, we perform a comparative study on recursive Bayesian filters for pedestrian path prediction at short time horizons (< 2s). We consider Extended Kalman Filters (EKF) based on single dynamical models and Interacting Multiple Models (IMM) combining several such basic models (constant velocity/acceleration/turn). These are applied to four typical pedestrian m...

Path Loss Prediction by Robust Regression Methods

Pedestrian Detection Using Structured SVM

With the advent of smart car and even driverless cars, the importance of intelligent driver’s system has been rapidly growing. Accordingly, driver’s vision has become one of the most popular issues and especially detecting some obstacles and pedestrians on the road are at the center of the vision problem in order to prevent accidents. Motivated by the importance of such topics, we implemented t...