PCA-RECT: An Energy-efficient Object Detection Approach for Event Cameras

We present the first purely event-based, energy-efficient approach for object detection and categorization using an event camera. Compared to traditional frame-based cameras, choosing event cameras results in high temporal resolution (order of microseconds), low power consumption (few hundred mW) and wide dynamic range (120 dB) as attractive properties. However, event-based object recognition systems are far behind their frame-based counterparts in terms of accuracy. To this end, this paper presents an event-based feature extraction method devised by accumulating local activity across the image frame and then applying principal component analysis (PCA) to the normalized neighborhood region. Subsequently, we propose a backtracking-free k-d tree mechanism for efficient feature matching by taking advantage of the low-dimensionality of the feature representation. Additionally, the proposed k-d tree mechanism allows for feature selection to obtain a lower-dimensional dictionary representation when hardware resources are limited to implement dimensionality reduction. Consequently, the proposed system can be realized on a field-programmable gate array (FPGA) device leading to high performance over resource ratio. The proposed system is tested on real-world event-based datasets for object categorization, showing superior classification performance and relevance to state-of-the-art algorithms. Additionally, we verified the object detection method and real-time FPGA performance in lab settings under non-controlled illumination conditions with limited training data and ground truth annotations.Comment: Accepted in ACCV 2018 Workshops, to appea

Applying neuromorphic vision sensors to planetary landing tasks

Recently there has been an increasing interest in application of bio-mimetic controller s and neuromorphic vision sensor s to planetary landing tasks. Within this context, we present combined low-level (SPICE) and high-level (behavioral) simulations of a novel neuromorphic VLSI vision sensor in a realistic planetary landing scenar io. We use results from low level simulations to build an abstr act descr iption of the chip which can be used in higher level simulations which include closed-loop control of the cr aft

Age and growth in a European flagship amphibian : equal performance at agricultural ponds and favourably managed aquatic sites

In human-modified landscapes, little is known about the influence of aquatic habitat types on the demographic structure of residing amphibian populations. In the present paper, we focus on a European flagship urodele species (the great crested newt Triturus cristatus) at the north-western range of its distribution, applying the method of skeletochronology to compare the ages of individuals retrieved from agricultural ponds with individuals retrieved from aquatic sites favourably managed for T. cristatus presence. Median ages ranged between 4.5 and 10.0 years depending on sex and population, and did not differ between the two site categories. Females were on average older than males at both agricultural ponds as well as favourably managed sites. Median ages at sexual maturity (3 years for females and 2 years for males) were 4 years below the most commonly observed age cohort in both sexes, suggesting that young adults regularly forgo reproduction. Mean body size did not differ between agricultural ponds and favourably managed sites. However, the former were characterised by a higher variance in body size, which is possibly linked to more unstable ecological conditions in agricultural settings. Taken together, our findings confirm that under suitable conditions agricultural ponds can harbour sustainable populations, an important finding for the broad-scale conservation management of T. cristatus which does not usually take population demographies into account. Keywords: Demography Great crested newt Skeletochronology Triturus cristatus Urodele

Event-based Asynchronous Sparse Convolutional Networks

Event cameras are bio-inspired sensors that respond to per-pixel brightness changes in the form of asynchronous and sparse "events". Recently, pattern recognition algorithms, such as learning-based methods, have made significant progress with event cameras by converting events into synchronous dense, image-like representations and applying traditional machine learning methods developed for standard cameras. However, these approaches discard the spatial and temporal sparsity inherent in event data at the cost of higher computational complexity and latency. In this work, we present a general framework for converting models trained on synchronous image-like event representations into asynchronous models with identical output, thus directly leveraging the intrinsic asynchronous and sparse nature of the event data. We show both theoretically and experimentally that this drastically reduces the computational complexity and latency of high-capacity, synchronous neural networks without sacrificing accuracy. In addition, our framework has several desirable characteristics: (i) it exploits spatio-temporal sparsity of events explicitly, (ii) it is agnostic to the event representation, network architecture, and task, and (iii) it does not require any train-time change, since it is compatible with the standard neural networks' training process. We thoroughly validate the proposed framework on two computer vision tasks: object detection and object recognition. In these tasks, we reduce the computational complexity up to 20 times with respect to high-latency neural networks. At the same time, we outperform state-of-the-art asynchronous approaches up to 24% in prediction accuracy

Inceptive Event Time-Surfaces for Object Classification Using Neuromorphic Cameras

This paper presents a novel fusion of low-level approaches for dimensionality reduction into an effective approach for high-level objects in neuromorphic camera data called Inceptive Event Time-Surfaces (IETS). IETSs overcome several limitations of conventional time-surfaces by increasing robustness to noise, promoting spatial consistency, and improving the temporal localization of (moving) edges. Combining IETS with transfer learning improves state-of-the-art performance on the challenging problem of object classification utilizing event camera data

Video synthesis from Intensity and Event Frames

Event cameras, neuromorphic devices that naturally respond to brightness changes, have multiple advantages with respect to traditional cameras. However, the difficulty of applying traditional computer vision algorithms on event data limits their usability. Therefore, in this paper we investigate the use of a deep learning-based architecture that combines an initial grayscale frame and a series of event data to estimate the following intensity frames. In particular, a fully-convolutional encoder-decoder network is employed and evaluated for the frame synthesis task on an automotive event-based dataset. Performance obtained with pixel-wise metrics confirms the quality of the images synthesized by the proposed architecture

The Ontology Lookup Service: more data and better tools for controlled vocabulary queries

The Ontology Lookup Service (OLS) (http://www.ebi.ac.uk/ols) provides interactive and programmatic interfaces to query, browse and navigate an ever increasing number of biomedical ontologies and controlled vocabularies. The volume of data available for querying has more than quadrupled since it went into production and OLS functionality has been integrated into several high-usage databases and data entry tools. Improvements have been made to both OLS query interfaces, based on user feedback and requirements, to improve usability and service interoperability and provide novel ways to perform queries

Analysis of the three most prevalent injuries in Australian football demonstrates a season to season association between groin/hip/ osteitis pubis injuries with ACL knee injuries

BACKGROUND: Injuries are common in contact sports like Australian football. The Australian Football League (AFL) has developed an extensive injury surveillance database that can be used for epidemiological studies. OBJECTIVES: The purpose of this study is to identify any association between the three most prevalent injuries in the AFL. PATIENTS AND METHODS: From the AFL injury surveillance data 1997-2012 the injury incidence (new injuries per club per season) and the injury prevalence data (missed games per club per season) were analysed to detect the three most common injuries that would cause a player to miss a match in the AFL. The three most prevalent injuries in the AFL are hamstring strains, groin/hip/osteitis pubis injuries and Anterior Cruciate Ligament (ACL) knee injuries. Following this, further study was undertaken to detect the presence of any statistical relationship between injury incidences of the three most prevalent injuries over this sixteen year study period. RESULTS: Statistical analysis demonstrates for any given year that there was an association between having a groin/hip/osteitis pubis injuriy and having a knee ACL injury (P < 0.05) over the entire sixteen years. In other words if the number of groin/hip/osteitis pubis injuries in any given season were higher than average (alternatively lower) then the number of knee ACL injuries were also higher than average (alternatively lower) for that same season. Hamstring injuries had the highest variance of incidence of the three most prevalent injuries. CONCLUSIONS: Analysis of the AFL injury data demonstrates an association between incidence of groin/hip/osteitis pubis injuries and incidence of knee ACL injuries for any given playing season. This finding is difficult to explain with further research being required.Geoffrey M. Verral, Adrian Esterman, Timothy E. Hewet