Localization is an essential problem in autonomous navigation of self-driving cars. We present a monocular vision based approach for localization in urban environments using road markings. We utilize road markings as landmarks instead of traditional visual features (e.g. SIFT) to tackle the localization problem because road markings are more robust against changes in perspective, illumination, and across time. Specifically, we employ Chamfer matching to register edges of road markings against a lightweight 3D map where road markings are represented as a set of sparse points. By only matching geometry of road markings, our localization algorithm further gains robustness against photometric appearance changes in the environment. We take vehicle odometry and epipolar geometry constraints into account and formulate a non-linear optimization problem to estimate the 6 DoF camera pose. We evaluate the proposed method on data collected in the real world. Experimental results show that our method achieves sub-meter localization errors in areas with sufficient road markings.

We present a method to predict long-term motion of pedestrians, modeling their behavior as jump-Markov processes with their goal a hidden variable. Assuming approximately rational behavior, and incorporating environmental constraints and biases, including time-varying ones imposed by traffic lights, we model intent as a policy in a Markov decision process framework. We infer pedestrian state using a Rao-Blackwellized filter, and intent by planning according to a stochastic policy, reflecting individual preferences in aiming at the same goal.

We address the problem of partially occluded object detection by implementing a model which includes latent visibility flags that are attached to cells and parts of a Deformable Part Model (DPM) [1]. A visibility flag indicates whether an image portion is part of a target object or part of an occluder. To compute the visibility flags and the score of the detector simultaneously, we maximize a concave objective function that is composed of the following four terms: (1) the detection scores of visible cells and parts, (2) a cell-to-cell consistency term which encourages neighboring cells to have the same visibility flags, (3) a cell-to-part consistency term which encourages compatible labeling among overlapping cells and parts, and (4) a penalty term for cells and parts that are labeled as occluded. The maximization of the concave objective function is done using the Alternating Direction Method of Multipliers (ADMM). By removing scores of occluded cells and parts from the final detection score we significantly improve detection performance on partially occluded pedestrians. In experiments we show that our system outperforms the standard DPM and other state-of-art methods on a benchmark database of partially occluded pedestrians.

We propose techniques for designing and training of pose-invariant object recognition systems using realistic 3d computer graphics models. We look at the relation between the size of the training set and the classification accuracy for a basic recognition task and provide a method for estimating the degree of difficulty of detecting an object. We show how to sample, align, and cluster images of objects on the view sphere. We address the problem of training on large, highly redundant data and propose a novel active learning method which generates compact training sets and compact classifiers. © 2009. The copyright of this document resides with its authors.

We present a shape matching algorithm based on the chamfer distance transform which can be easily integrated into the well-known SIFT framework. The shape matching was designed to overcome the limitations of SIFT matching for objects which lack texture and have the majority of their features located on the object boundary.

The problem of learning is arguably at the very core of the problem of intelligence, both biological and artificial. In this paper we review our approach to the problem of visual perception based on supervised learning. After a brief presentation of the theoretical background, we focus on some of the engineering applications of statistical learning to computer vision and discuss the main open problems and directions of our future research.

We present a two-step method to speed-up object detection systems in computer vision that use Support Vector Machines (SVMs) as classifiers. In a first step we perform feature reduction by choosing relevant image features according to a measure derived from statistical learning theory. In a second step we build a hierarchy of classifiers. On the bottom level, a simple and fast classifier analyzes the whole image and rejects large parts of the background On the top level, a slower but more accurate classifier performs the final detection. Experiments with a face detection system show that combining feature reduction with hierarchical classification leads to a speed-up by a factor of 170 with similar classification performance.

We present a component-based, trainable system for detecting frontal and near-frontal views of faces in still gray images. The system consists of a two-level hierarchy of Support Vector Machine (SVM) classifiers. On the first level, component classifiers independently detect components Of a face. On the second level, a single classifier checks if the geometrical configuration of the detected components in the image matches a geometrical model of a face. We propose a method for automatically learning components by using 3-D head models, This approach has the advantage that no manual interaction is required for choosing and extracting components. Experiments show that the component-based system is significantly more robust against rotations in depth than a comparable system trained on whole face patterns.

We present a component-based method and two global methods for face recognition and evaluate them with respect to robustness against pose changes. In the component system we first locate facial components, extract them and combine them into a single feature vector which is classified by a Support Vector Machine (SVM). The two global systems recognize faces by classifying a single feature vector consisting of the gray values of the whole face image. In the first global system we trained a single SVM classifier for each person in the database. The second system consists of sets of viewpoint-specific SVM classifiers and involves clustering during training. We performed extensive tests on a database which included faces rotated up to about 40/spl deg/ in depth. The component system clearly outperformed both global systems on all tests.

We describe an algorithm for automatically learning discriminative com- ponents of objects with SVM classifiers. It is based on growing image parts by minimizing theoretical bounds on the error probability of an SVM. Component-based face classifiers are then combined in a second stage to yield a hierarchical SVM classifier. Experimental results in face classification show considerable robustness against rotations in depth and suggest performance at significantly better level than other face detection systems. Novel aspects of our approach are: a) an algorithm to learn component-based classification experts and their combination, b) the use of 3-D morphable models for training, and c) a maximum operation on the output of each component classifier which may be relevant for bio- logical models of visual recognition.

In this paper we present an algorithm for recognizing walking pedestrians in sequences of color images taken from a moving camera. The recognition is based on the characteristic motion of the legs of a pedestrian walking parallel to the image plane. Each image is segmented into region-like image parts by clustering pixels in a combined color/position feature space. The proposed clustering technique implies matching of corresponding clusters in consecutive frames and therefore allows clusters to be tracked over a sequence of images. Based on the observation of clusters over time a two-stage classifier extracts those clusters which most likely represent the legs of pedestrians. A fast polynomial classifier performs a rough preselection of clusters by evaluating temporal changes of a shape-dependent clusters feature. The final classification is done by a time delay neural network (TDNN) with spatio-temporal receptive fields.

In this contribution we present an algorithm for tracking non-rigid, moving objects in a sequence of colored images, which were recorded by a non-stationary camera. The application background is vision-based driving assistance in the inner city. In an initial step, object parts are determined by a divisive clustering algorithm, which is applied to all pixels in the first image of the sequence. The feature space is defined by the color and position of a pixel. For each new image the clusters of the previous image are adapted iteratively by a parallel k-means clustering algorithm. Instead of tracking single points, edges, or areas over a sequence of images, only the centroids of the clusters are tracked. The proposed method remarkably simplifies the correspondence problem and also ensures a robust tracking behaviour.