Spatio-Contextual Deep Network-Based Multimodal Pedestrian Detection for Autonomous Driving

GPU-based Pedestrian Detection for Autonomous Driving

We propose a real-time pedestrian detection system for the embedded Nvidia Tegra X1 GPUCPU hybrid platform. The pipeline is composed by the following state-of-the-art algorithms: Histogram of Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG) features extracted from the input image; Pyramidal Sliding Window technique for candidate generation; and Support Vector Machine (SVM)...

Vision-Based Pedestrian Detection for Driving Assistance

This survey focuses on some of the most important and recent algorithms for pedestrian detection in video sequences taken from a camera mounted on a car. The longterm goal of this line of research is to develop vision-based systems that could be successfully implemented in cars for driver support in urban scenes or for autonomous driving. Some of the surveyed systems have already proved their e...

Pedestrian Detection with Deep Convolutional Neural Network

The problem of pedestrian detection in image and video frames has been extensively investigated in the past decade. However, the low performance in complex scenes shows that it remains an open problem. In this paper, we propose to cascade simple Aggregated Channel Features (ACF) and rich Deep Convolutional Neural Network (DCNN) features for efficient and effective pedestrian detection in comple...

Object Detection for Autonomous Driving Using Deep Learning

In Teacher We Trust: Deep Network Compression for Pedestrian Detection

Deep convolutional neural networks continue to advance the state-of-the-art in many domains as they grow bigger and more complex. It has been observed that many of the parameters of a large network are redundant, allowing for the possibility of learning a smaller network that mimics the outputs of the large network through a process called Knowledge Distillation. We show, however, that standard...