Manuel Marques — Page 2

arXiv2017

Subspace Segmentation by Successive Approximations: A Method for Low-Rank and High-Rank Data with Missing Entries

We propose a method to reconstruct and cluster incomplete high-dimensional data lying in a union of low-dimensional subspaces. Exploring the sparse representation model, we jointly estimate the missing data while imposing the intrinsic subspace structure. Since we have a non-convex problem, we propose an iterative method to reconstruct the data and provide a sparse similarity affinity matrix. This method is robust to initialization and achieves greater reconstruction accuracy than current methods, which dramatically improves clustering performance. Extensive experiments with synthetic and real data show that our approach leads to significant improvements in the reconstruction and segmentation, outperforming current state of the art for both low and high-rank data.

cs.CV↗

arXiv2017

A Context Aware and Video-Based Risk Descriptor for Cyclists

Aiming to reduce pollutant emissions, bicycles are regaining popularity specially in urban areas. However, the number of cyclists' fatalities is not showing the same decreasing trend as the other traffic groups. Hence, monitoring cyclists' data appears as a keystone to foster urban cyclists' safety by helping urban planners to design safer cyclist routes. In this work, we propose a fully image-based framework to assess the rout risk from the cyclist perspective. From smartphone sequences of images, this generic framework is able to automatically identify events considering different risk criteria based on the cyclist's motion and object detection. Moreover, since it is entirely based on images, our method provides context on the situation and is independent from the expertise level of the cyclist. Additionally, we build on an existing platform and introduce several improvements on its mobile app to acquire smartphone sensor data, including video. From the inertial sensor data, we automatically detect the route segments performed by bicycle, applying behavior analysis techniques. We test our methods on real data, attaining very promising results in terms of risk classification, according to two different criteria, and behavior analysis accuracy.

cs.CV↗