A Pose-Sensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-Ranking

Person re identification is a challenging retrieval task that requires matching a person's acquired image across non overlapping camera views. In this paper we propose an effective approach that incorporates both the fine and coarse pose information of the person to learn a discriminative embedding. In contrast to the recent direction of explicitly modeling body parts or correcting for misalignment based on these, we show that a rather straightforward inclusion of acquired camera view and/or the detected joint locations into a convolutional neural network helps to learn a very effective representation. To increase retrieval performance, re-ranking techniques based on computed distances have recently gained much attention. We propose a new unsupervised and automatic re-ranking framework that achieves state-of-the-art re-ranking performance. We show that in contrast to the current state-of-the-art re-ranking methods our approach does not require to compute new rank lists for each image pair (e.g., based on reciprocal neighbors) and performs well by using simple direct rank list based comparison or even by just using the already computed euclidean distances between the images. We show that both our learned representation and our re-ranking method achieve state-of-the-art performance on a number of challenging surveillance image and video datasets. The code is available online at: https://github.com/pse-ecn/pose-sensitive-embeddingComment: CVPR 2018: v2 (fixes, added new results on PRW dataset

Confidence Estimation in Image-Based Localization

Image-based localization aims at estimating the camera position and orientation, briefly referred as camera pose, from a given image. Estimating the camera pose is needed in several applications, such as augmented reality, odometry and self-driving cars. A main challenge is to develop an algorithm for large varying environments, such as buildings or whole cities. During the past decade several algorithms have tackled this challenge and, despite the promising results, the task is far from being solved. Several applications, however, need a reliable pose estimate; in odometry applications, for example, the camera pose is used to correct the drift error accumulated by inertial sensor measurements. Based on this, it is important to be able to assess the confidence of the estimated pose and manage to discriminate between correct and incorrect poses within a prefixed error threshold. A common approach is to use the number of inliers produced in the RANSAC loop to evaluate how good an estimate is. Particularly, this is used to choose the best pose from a given image from a set of candidates. This metric, however, is not very robust, especially for indoor scenes, presenting several repetitive patterns, such as long textureless walls or similar objects. Despite some other metrics have been proposed, they aim at improving the accuracy of the algorithm, by grading candidate poses referred to the same query image; they thus recognize the best pose among a given set but cannot be used to grade the overall confidence of the final pose. In this thesis, we formalize confidence estimation as a binary classification problem and investigate how to quantify the confidence of an estimated camera pose. Opposed to the previous work, this new research question takes place after the whole visual localization pipeline and is able to compare also poses from different query images. In addition to the number of inliers, other factors such as the spatial distributions of inliers, are considered. A neural network is then used to generate a novel robust metric, able to evaluate the confidence for different query images. The proposed method is benchmarked using InLoc, a challenging dataset for indoor pose estimation. It is also shown the proposed confidence metric is independent of the dataset used for training and can be applied to different datasets and pipelines

A Survey on Global LiDAR Localization

Knowledge about the own pose is key for all mobile robot applications. Thus pose estimation is part of the core functionalities of mobile robots. In the last two decades, LiDAR scanners have become a standard sensor for robot localization and mapping. This article surveys recent progress and advances in LiDAR-based global localization. We start with the problem formulation and explore the application scope. We then present the methodology review covering various global localization topics, such as maps, descriptor extraction, and consistency checks. The contents are organized under three themes. The first is the combination of global place retrieval and local pose estimation. Then the second theme is upgrading single-shot measurement to sequential ones for sequential global localization. The third theme is extending single-robot global localization to cross-robot localization on multi-robot systems. We end this survey with a discussion of open challenges and promising directions on global lidar localization

InLoc: Indoor Visual Localization with Dense Matching and View Synthesis

We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph with respect to a large indoor 3D map. The contributions of this work are three-fold. First, we develop a new large-scale visual localization method targeted for indoor environments. The method proceeds along three steps: (i) efficient retrieval of candidate poses that ensures scalability to large-scale environments, (ii) pose estimation using dense matching rather than local features to deal with textureless indoor scenes, and (iii) pose verification by virtual view synthesis to cope with significant changes in viewpoint, scene layout, and occluders. Second, we collect a new dataset with reference 6DoF poses for large-scale indoor localization. Query photographs are captured by mobile phones at a different time than the reference 3D map, thus presenting a realistic indoor localization scenario. Third, we demonstrate that our method significantly outperforms current state-of-the-art indoor localization approaches on this new challenging data