CONVOLUTIONAL AND RECURRENT NEURAL NETWORKS FOR PEDESTRIAN DETECTION

Abstract

Pedestrian Detection in real time has become an interesting and a challenging problem lately. With the advent of autonomous vehicles and intelligent traffic monitoring systems, more time and money are being invested into detecting and locating pedestrians for their safety and towards achieving complete autonomy in vehicles. For the task of pedestrian detection, Convolutional Neural Networks (ConvNets) have been very promising over the past decade. ConvNets have a typical feed-forward structure and they share many properties with the visual system of the human brain. On the other hand, Recurrent Neural Networks (RNNs) are emerging as an important technique for image based detection problems and they are more closely related to the visual system due to their recurrent connections. Detecting pedestrians in a real time environment is a task where sequence is very important and it is intriguing to see how ConvNets and RNNs handle this task. This thesis hopes to make a detailed comparison between ConvNets and RNNs for pedestrian detection, how both these techniques perform on sequential pedestrian data, their scopes of research and what are their advantages and disadvantages. The comparison is done on two benchmark datasets - TUD-Brussels and ETH Pedestrian Datasets and a comprehensive evaluation is presented to see how research on these topics can be taken forward.