Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world

New perspectives and methods for stream learning in the presence of concept drift.

153 p.Applications that generate data in the form of fast streams from non-stationary environments, that is,those where the underlying phenomena change over time, are becoming increasingly prevalent. In thiskind of environments the probability density function of the data-generating process may change overtime, producing a drift. This causes that predictive models trained over these stream data become obsoleteand do not adapt suitably to the new distribution. Specially in online learning scenarios, there is apressing need for new algorithms that adapt to this change as fast as possible, while maintaining goodperformance scores. Examples of these applications include making inferences or predictions based onfinancial data, energy demand and climate data analysis, web usage or sensor network monitoring, andmalware/spam detection, among many others.Online learning and concept drift are two of the most hot topics in the recent literature due to theirrelevance for the so-called Big Data paradigm, where nowadays we can find an increasing number ofapplications based on training data continuously available, named as data streams. Thus, learning in nonstationaryenvironments requires adaptive or evolving approaches that can monitor and track theunderlying changes, and adapt a model to accommodate those changes accordingly. In this effort, Iprovide in this thesis a comprehensive state-of-the-art approaches as well as I identify the most relevantopen challenges in the literature, while focusing on addressing three of them by providing innovativeperspectives and methods.This thesis provides with a complete overview of several related fields, and tackles several openchallenges that have been identified in the very recent state of the art. Concretely, it presents aninnovative way to generate artificial diversity in ensembles, a set of necessary adaptations andimprovements for spiking neural networks in order to be used in online learning scenarios, and finally, adrift detector based on this former algorithm. All of these approaches together constitute an innovativework aimed at presenting new perspectives and methods for the field

Asynchronous dual-pipeline deep learning framework for online data stream classification

Data streaming classification has become an essential task in many fields where real-time decisions have to be made based on incoming information. Neural networks are a particularly suitable technique for the streaming scenario due to their incremental learning nature. However, the high computation cost of deep architectures limits their applicability to high-velocity streams, hence they have not yet been fully explored in the literature. Therefore, in this work, we aim to evaluate the effectiveness of complex deep neural networks for supervised classification in the streaming context. We propose an asynchronous deep learning framework in which training and testing are performed simultaneously in two different processes. The data stream entering the system is dual fed into both layers in order to concurrently provide quick predictions and update the deep learning model. This separation reduces processing time while obtaining high accuracy on classification. Several time-series datasets from the UCR repository have been simulated as streams to evaluate our proposal, which has been compared to other methods such as Hoeffding trees, drift detectors, and ensemble models. The statistical analysis carried out verifies the improvement in performance achieved with our dual-pipeline deep learning framework, that is also competitive in terms of computation time.Ministerio de Economía y Competitividad TIN2017-88209-C2-2-

Deep Learning for 2D and 3D Scene Understanding

This thesis comprises a body of work that investigates the use of deep learning for 2D and 3D scene understanding. Although there has been significant progress made in computer vision using deep learning, a lot of that progress has been relative to performance benchmarks, and for static images; it is common to find that good performance on one benchmark does not necessarily mean good generalization to the kind of viewing conditions that might be encountered by an autonomous robot or agent. In this thesis, we address a variety of problems motivated by the desire to see deep learning algorithms generalize better to robotic vision scenarios. Specifically, we span topics of multi-object detection, unsupervised domain adaptation for semantic segmentation, video object segmentation, and semantic scene completion. First, most modern object detectors use a final post-processing step known as Non-maximum suppression (GreedyNMS). This suffers an inevitable trade-off between precision and recall in crowded scenes. To overcome this limitation, we propose a Pairwise-NMS to cure GreedyNMS. Specifically, a pairwise-relationship network that is based on deep learning is learned to predict if two overlapping proposal boxes contain two objects or zero/one object, which can handle multiple overlapping objects effectively. A common issue in training deep neural networks is the need for large training sets. One approach to this is to use simulated image and video data, but this suffers from a domain gap wherein the performance on real-world data is poor relative to performance on the simulation data. We target a few approaches to addressing so-called domain adaptation for semantic segmentation: (1) Single and multi-exemplars are employed for each class in order to cluster the per-pixel features in the embedding space; (2) Class-balanced self-training strategy is utilized for generating pseudo labels in the target domain; (3) Moreover, a convolutional adaptor is adopted to enforce the features in the source domain and target domain are closed with each other. Next, we tackle the video object segmentation by formulating it as a meta-learning problem, where the base learner aims to learn semantic scene understanding for general objects, and the meta learner quickly adapts the appearance of the target object with a few examples. Our proposed meta-learning method uses a closed-form optimizer, the so-called \ridge regression", which is conducive to fast and better training convergence. One-shot video object segmentation (OSVOS) has the limitation to \overemphasize" the generic semantic object information while \diluting" the instance cues of the object(s), which largely block the whole training process. Through adding a common module, video loss, which we formulate with various forms of constraints (including weighted BCE loss, high-dimensional triplet loss, as well as a novel mixed instance-aware video loss), to train the parent network, the network is then better prepared for the online fine-tuning. Next, we introduce a light-weight Dimensional Decomposition Residual network (DDR) for 3D dense prediction tasks. The novel factorized convolution layer is effective for reducing the network parameters, and the proposed multi-scale fusion mechanism for depth and color image can improve the completion and segmentation accuracy simultaneously. Moreover, we propose PALNet, a novel hybrid network for Semantic Scene Completion(SSC) based on single depth. PALNet utilizes a two-stream network to extract both 2D and 3D features from multi-stages using fine-grained depth information to eficiently capture the context, as well as the geometric cues of the scene. Position Aware Loss (PA-Loss) considers Local Geometric Anisotropy to determine the importance of different positions within the scene. It is beneficial for recovering key details like the boundaries of objects and the corners of the scene. Finally, we propose a 3D gated recurrent fusion network (GRFNet), which learns to adaptively select and fuse the relevant information from depth and RGB by making use of the gate and memory modules. Based on the single-stage fusion, we further propose a multi-stage fusion strategy, which could model the correlations among different stages within the network.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202