Detection and intention prediction of pedestrians in zebra crossings

Sammanfattning: Behavior of pedestrians who are moving or standing still close to the street could be one of the most significant indicators about pedestrian’s instant future actions. Being able to recognize the activity of a pedestrian, can reveal significant information about pedestrian’s crossing intentions. Thus, the scope of this thesisis to investigate ways and methods to improve understanding ofpedestrian´s activity and in particular detecting their motion and head orientation in relation to the surrounding traffic. Furthermore, different features and methods are examined, used and assessed according to their contribution on distinguishing between different actions. Feature extraction methods considered are Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) and Convolutional Neural Networks (CNNs). The features are extracted by processing still images of pedestrians from the Joint Attention for Autonomous Driving (JAAD) dataset. The images are extracted from video frames depicting pedestrians walking next to the road or crossing the road are used. Based on the features, a number of Machine Learning (ML) techniques(CNN, Artificial Neural Networks, Support Vector Machines, K-Nearest Neighbor and Decision Trees) are used to predict the head orientation, motion as well as the intention of the pedestrian. The work is divided into three parts, the first is to combine feature extraction and ML to predict pedestrian’s action regarding if they are walking or not. The second is to identify the pedestrian's head orientation in terms of if he/she is looking at the vehicle or not, this is also done by combining feature extraction and ML. The final task is to combine these two measures in a ML-basedclassifier that is trained to predict the pedestrian´s crossing intention and action. In addition to the pedestrian’s behavior for estimating the crossing intention, additional features about the local environment were added as input signals for the classifier, for instance, information about the presence of zebra markings in the street, the location of the scene, and weather conditions.