Table of Contents
Smart Cars for Safe Pedestrians
One of the most significant large-scale deployments of intelligent systems in our daily life nowadays involves driver assistance in smart cars. Accident statistics show that roughly one quarter of all traffic fatalities world-wide involve vulnerable road users (pedestrians, bicyclists); most accidents occur in an urban setting. Devising an effective driver assistance system for vulnerable road users has long been impeded, however, by the “perception bottleneck”, i.e. not being able to detect and localize vulnerable road users sufficiently accurate. The problem is challenging due to the large variation in object appearance, the dynamic and cluttered urban backgrounds, and the potentially irregular object motion. Topping these off are stringent performance criteria and real-time constraints. I give an overview of the remarkable computer vision progress that has been achieved in this area and discuss the main enablers: the algorithms, the data, the hardware and the tests. Daimler has recently introduced an advanced set of driver assistance functions in its Mercedes-Benz 2013-2014 S-, E-, and C-Class models, termed “Intelligent Drive”, using stereo vision. It includes a pedestrian safety component which facilitates fully automatic emergency braking - the system works day and night. I discuss “Intelligent Drive” and future research directions, on the road towards accident-free driving. Bio
Dariu M. Gavrila received the PhD degree in computer science from the University of Maryland at College Park, USA, in 1996. Since 1997, he has been with Daimler R&D in Ulm, Germany, where he is currently a Principal Scientist. In 2003, he was further appointed professor at the University of Amsterdam, chairing the area of Intelligent Perception Systems (part time). Over the past 15 years, Prof. Gavrila has focused on visual systems for detecting humans and their activity, with application to intelligent vehicles, smart surveillance and social robotics. He led the multi-year pedestrian detection research effort at Daimler, which materialized in the Mercedes-Benz S-, E-, and C-Class models (2013-2014). He is frequently cited in the scientific literature and he received the I/O 2007 Award from the Netherlands Organization for Scientific Research (NWO) as well as several conference paper awards. His personal Web site is www.gavrila.net.
Part I: Transformation Pursuit for Image Classification
Part II: DeepFlow, Convolutional Large-scale Displacement Optical Flow
In part I, I present a simple and efficient algorithm – Image Transformation Pursuit (ITP) – that performs automatic selection of relevant transformations for virtual example generation in order to enforce transformation-invariance in visual recognition architectures. We report impressive performance gains on two public visual recognition benchmarks : the CUB dataset of bird images, and the ImageNet2010 challenge dataset. Joint work with M. Paulin, J. Revaud, F. Perronnin, C. Schmid.
In part II, I present a new approach for large-scale displacement optical flow estimation, called DeepFlow. The approach blends a matching algorithm, DeepMatching, inspired by convolutional nets, with variational energy minimization. The resulting algorithm shows competitive performance on optical ﬂow public benchmarks, and sets a new state-of-the-art on the MPI-Sintel benchmark dataset.
Joint work with P. Weinzaepfel, J. Revaud, C. Schmid.
Title: Efficient training of structured SVMs via soft constraints.
Abstract: Structured output prediction is a powerful framework for jointly predicting interdependent output labels. Learning the parameters of structured predictors is a central task in machine learning applications. However, training the model from data often becomes computationally expensive. Several methods have been proposed to exploit the model structure, or decomposition, in order to obtain efficient training algorithms. In particular, methods based on linear programming relaxation, or dual decomposition, decompose the prediction task into multiple simpler prediction tasks and enforce agreement between overlapping predictions. In this work we observe that relaxing these agreement constraints and replacing them with soft constraints yields a much easier optimization problem. Based on this insight we propose an alternative training objective, analyze its theoretical properties, and derive an algorithm for its optimization. Our method, based on the Frank-Wolfe algorithm, achieves significant speedups over existing state-of-the-art methods without hurting prediction accuracy.