PEDESTRIAN WALKING DIRECTION DETECTION USING HYBRID CNN-SVM MODEL

Abstract

Pedestrian movement direction recognition is an important factor in autonomous driver assistance and security surveillance systems. Pedestrians are the most crucial and fragile moving objects in streets, roads, and events, where thousands of people may gather on a regular basis. People flow analysis on zebra crossings and in shopping centers or events such as demonstrations are a key element to improve safety and to enable autonomous cars to drive in real life environments. This thesis focuses on deep learning techniques such as hybrid Convolutional Neural Networks (CNN) – Support Vector Machine (SVM) model to achieve a reliable detection of pedestrians moving in a particular direction. We propose a CNN-based technique that leverages current pedestrian detection techniques (histograms of oriented gradients-linear SVM) to generate a sum of subtracted frames (flow estimation around the detected pedestrian), which are used as an input for the hybrid CNN – SVM model.