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Total Recall: Understanding Traffic Signs using Deep Hierarchical Convolutional Neural Networks

, ,  - 2018

Recognizing Traffic Signs using intelligent systems can drastically reduce the number of accidents happening world-wide. With the arrival of Self-driving cars it has become a staple challenge to solve the automatic recognition of Traffic and Hand-held signs in the major streets. Various machine learning techniques like Random Forest, SVM as well ...


Systematic evaluation of CNN advances on the ImageNet

, ,  - 2016

The paper systematically studies the impact of a range of recent advances in CNN architectures and learning methods on the object categorization (ILSVRC) problem. The evalution tests the influence of the following choices of the architecture: non-linearity (ReLU, ELU, maxout, compatibility with batch normalization), pooling variants (stochastic, ...


Optimal Testing of Self-Driving Cars

, ,  - 2017

Automotive manufacturers attempting to bring autonomous vehicles to market must make the case that their product is sufficiently safe for public deployment. Much of this case will likely rely upon outcomes from real-world testing, requiring manufacturers to be strategic about how they allocate testing resources in order to maximize their chances ...


Traffic Sign Classification Using Deep Inception Based Convolutional Networks

 - 2015

In this work, we propose a novel deep network for traffic sign classification that achieves outstanding performance on GTSRB surpassing all previous methods. Our deep network consists of spatial transformer layers and a modified version of inception module specifically designed for capturing local and global features together. This features adopt...


Deep Attention Recurrent Q-Network

, , , ,  - 2015

A deep learning approach to reinforcement learning led to a general learner able to train on visual input to play a variety of arcade games at the human and superhuman levels. Its creators at the Google DeepMind's team called the approach: Deep Q-Network (DQN). We present an extension of DQN by "soft" and "hard" attention mechanisms. Tests of the...


Evolving Boxes for Fast Vehicle Detection

, , , , ,  - 2017

We perform fast vehicle detection from traffic surveillance cameras. A novel deep learning framework, namely Evolving Boxes, is developed that proposes and refines the object boxes under different feature representations. Specifically, our framework is embedded with a light-weight proposal network to generate initial anchor boxes as well as to ea...


Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks

, , , ,  - 2018

Understanding human motion behavior is critical for autonomous moving platforms (like self-driving cars and social robots) if they are to navigate human-centric environments. This is challenging because human motion is inherently multimodal: given a history of human motion paths, there are many socially plausible ways that people could move in th...


A Minimal Closed-Form Solution for Multi-Perspective Pose Estimation using Points and Lines

, ,  - 2018

We propose a minimal solution for pose estimation using both points and lines for a multi-perspective camera. In this paper, we treat the multi-perspective camera as a collection of rigidly attached perspective cameras. These type of imaging devices are useful for several computer vision applications that require a large coverage such as surveill...


MultiCol-SLAM - A Modular Real-Time Multi-Camera SLAM System

,  - 2016

The basis for most vision based applications like robotics, self-driving cars and potentially augmented and virtual reality is a robust, continuous estimation of the position and orientation of a camera system w.r.t the observed environment (scene). In recent years many vision based systems that perform simultaneous localization and mapping (SLAM...


An Empirical Evaluation of Deep Learning on Highway Driving

, , , , , , , , , , , ,  - 2015

Numerous groups have applied a variety of deep learning techniques to computer vision problems in highway perception scenarios. In this paper, we presented a number of empirical evaluations of recent deep learning advances. Computer vision, combined with deep learning, has the potential to bring about a relatively inexpensive, robust solution to ...