We explore the trade-offs of performing linear algebra using Apache Spark, compared to traditional C and MPI implementations on HPC platforms. Spark is designed for data analytics on cluster computing platforms with access to local disks and is optimized for data-parallel tasks. We examine three widely-used and important matrix factorizations: NMF (for physical plausability), PCA (for its ubiquity) and CX (for data interpretability). We apply these methods to 1.6TB particle physics, 2.2TB and 16TB climate modeling and 1.1TB bioimaging data. The data matrices are tall-and-skinny which enable the algorithms to map conveniently into Spark's data-parallel model. We perform scaling experiments on up to 1600 Cray XC40 nodes, describe the sources of slowdowns, and provide tuning guidance to obtain high performance.

A rail yard is a dangerous environment for humans to work in, primarily because of the possibility of serious injuries associated with moving rail cars, locomotives, and uneven terrain. For robots to act autonomously in such environments, there exists a need for a perception system that can act reliably under uncertain conditions. This uncertainty arises from multiple factors—uncontrolled lighting, uneven terrain, variability in appearance of objects, dynamic environment, noisy sensors, and controllers. In this paper, we present a perception system for a mobile robot that leverages information from various sensing modalities to act reliably in a partially observable environment. We show how our system can detect brake levers on a rail car, by fusing information from multiple detectors. We validate our approach by performing tests in an actual rail yard over multiple days and nights.

We propose a novel method for improved object detection in a video. Our approach adapts a generic offline trained detector (OTD) to a specific test video by collecting online samples in an unsupervised manner. Most of the existing adaptation methods focus on collecting confident online samples and do not address how to deal with ambiguous and noisy online samples. We address the importance of collecting online samples which are true representative of the actual objects present in the video and propose a Boosted Multiple Instance Random Fern (B-MIRF) classifier as the adaptive classifier. Multiple Instance Learning (MIL) provides reliability for training with noisy online samples and boosting process enables in obtaining more discriminative random ferns. We apply B-MIRF classifier on the detection responses obtained from OTD, hence our method improves the performance by improving the precision of OTD. We evaluate performance of our method on two challenging public datasets and show better performance than other state of the art methods.

Detector adaptation is a challenging problem and several methods have been proposed in recent years. We propose multi class boosted random ferns for detector adaptation. First we collect online samples in an unsupervised manner and collected positive online samples are divided into different categories for different poses of the object. Then we train a multi-class boosted random fern adaptive classifier. Our adaptive classifier training focuses on two aspects: discriminability and efficiency. Boosting provides discriminative random ferns. For efficiency, our boosting procedure focuses on sharing the same feature among different classes and multiple strong classifiers are trained in a single boosting framework. Experiments on challenging public datasets demonstrate effectiveness of our approach.

In this work, we present a novel and efficient detector adaptation method which improves the performance of an offline trained classifier (baseline classifier) by adapting it to new test datasets. We address two critical aspects of adaptation methods: generalizability and computational efficiency. We propose an adaptation method, which can be applied to various baseline classifiers and is computationally efficient also. For a given test video, we collect online samples in an unsupervised manner and train a random fern adaptive classifier. The adaptive classifier improves precision of the baseline classifier by validating the obtained detection responses from baseline classifier as correct detections or false alarms. Experiments demonstrate generalizability, computational efficiency and effectiveness of our method, as we compare our method with state of the art approaches for the problem of human detection and show good performance with high computational efficiency on two different baseline classifiers.

Most common approaches for object detection collect thousands of training examples and train a detector in an offline setting, using supervised learning methods, with the objective of obtaining a generalized detector that would give good performance on various test datasets. However, when an offline trained detector is applied on challenging test datasets, it may fail in some cases by not being able to detect some objects or by producing false alarms. We propose an unsupervised multiple instance learning (MIL) based incremental solution to deal with this issue. We introduce an MIL loss function for Real Adaboost and present a tracking based effective unsupervised online sample collection mechanism to collect the online samples for incremental learning. Experiments demonstrate the effectiveness of our approach by improving the performance of a state of the art offline trained detector on the challenging datasets for pedestrian category.

In this paper we propose a vehicle detection framework on low resolution aerial range data. Our system consists of three steps: data mapping, 2D vehicle detection and postprocessing. First, we map the range data into 2D grayscale images by using the depth information only. For this purpose we propose a novel local ground plane estimation method, and the estimated ground plane is further refined by a global refinement process. Then we compute the depth value of missing points (points for which no depth information is available) by an effective interpolation method. In the second step, to train a classifier for the vehicles, we describe a method to generate more training examples from very few training annotations and adopt the fast cascade Adaboost approach for detecting vehicles in 2D grayscale images. Finally, in post-processing step we design a novel method to detect some vehicles which are comprised of clusters of missing points. We evaluate our method on real aerial data and the experiments demonstrate the effectiveness of our approach.