This paper documents the winning entry at the CVPR2017 vehicle velocity estimation challenge. Velocity estimation is an emerging task in autonomous driving which has not yet been thoroughly explored. The goal is to estimate the relative velocity of a specific vehicle from a sequence of images. In this paper, we present a light-weight approach for directly regressing vehicle velocities from their trajectories using a multilayer perceptron. Another contribution is an explorative study of features for monocular vehicle velocity estimation. We find that light-weight trajectory based features outperform depth and motion cues extracted from deep ConvNets, especially for far-distance predictions where current disparity and optical flow estimators are challenged significantly. Our light-weight approach is real-time capable on a single CPU and outperforms all competing entries in the velocity estimation challenge. On the test set, we report an average error of 1.12 m s which is comparable to a (ground-truth) system that combines LiDAR and radar techniques to achieve an error of around 0.71 m s.
Object tracking is one of the fundamental problems in computer vision and has been extensively studied @cite_25 and applied in many different tasks.
@cite_25
1995903777
Although motion analysis has been extensively investigated in the literature and a wide variety of tracking algorithms have been proposed, the problem of tracking objects using the Dynamic Vision Sensor requires a slightly different approach. Dynamic Vision Sensors are biologically inspired vision systems that asynchronously generate events upon relative light intensity changes. Unlike conventional vision systems, the output of such sensor is not an image (frame) but an address events stream. Therefore, most of the conventional tracking algorithms are not appropriate for the DVS data processing. In this paper, we introduce algorithm for spatiotemporal tracking that is suitable for Dynamic Vision Sensor. In particular, we address the problem of multiple persons tracking in the occurrence of high occlusions. We investigate the possibility to apply Gaussian Mixture Models for detection, description and tracking objects. Preliminary results prove that our approach can successfully track people even when their trajectories are intersecting. On-line boosting is one of the most successful on-line algorithms and thus applied in many computer vision applications. However, even though boosting, in general, is well known to be susceptible to class-label noise, on-line boosting is mostly applied to self-learning applications such as visual object tracking, where label-noise is an inherent problem. This paper studies the robustness of on-line boosting. Since mainly the applied loss function determines the behavior of boosting, we propose an on-line version of GradientBoost, which allows us to plug in arbitrary loss-functions into the on-line learner. Hence, we can easily study the importance and the behavior of different loss-functions. We evaluate various on-line boosting algorithms in form of a competitive study on standard machine learning problems as well as on common computer vision applications such as tracking and autonomous training of object detectors. Our results show that using on-line Gradient-Boost with robust loss functions leads to superior results in all our experiments. Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: 1) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; 2) longitudinal studies, where temporal structural or anatomical changes are investigated; and 3) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner. Abstract This article reviews the past 25 years of research on eye movements (1986–2011). Emphasis is on three oculomotor behaviors: gaze control, smooth pursuit and saccades, and on their interactions with vision. Focus over the past 25 years has remained on the fundamental and classical questions: What are the mechanisms that keep gaze stable with either stationary or moving targets? How does the motion of the image on the retina affect vision? Where do we look – and why – when performing a complex task? How can the world appear clear and stable despite continual movements of the eyes? The past 25 years of investigation of these questions has seen progress and transformations at all levels due to new approaches (behavioral, neural and theoretical) aimed at studying how eye movements cope with real-world visual and cognitive demands. The work has led to a better understanding of how prediction, learning and attention work with sensory signals to contribute to the effective operation of eye movements in visually rich environments.