Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks

This paper presents to the best of our knowledge the first end-to-end object tracking approach which directly maps from raw sensor input to object tracks in sensor space without requiring any feature engineering or system identification in the form of plant or sensor models. Specifically, our system accepts a stream of raw sensor data at one end and, in real-time, produces an estimate of the entire environment state at the output including even occluded objects. We achieve this by framing the problem as a deep learning task and exploit sequence models in the form of recurrent neural networks to learn a mapping from sensor measurements to object tracks. In particular, we propose a learning method based on a form of input dropout which allows learning in an unsupervised manner, only based on raw, occluded sensor data without access to ground-truth annotations. We demonstrate our approach using a synthetic dataset designed to mimic the task of tracking objects in 2D laser data -- as commonly encountered in robotics applications -- and show that it learns to track many dynamic objects despite occlusions and the presence of sensor noise.Comment: Published in The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), Video: https://youtu.be/cdeWCpfUGWc, Code: http://mrg.robots.ox.ac.uk/mrg_people/peter-ondruska

End-to-End Tracking and Semantic Segmentation Using Recurrent Neural Networks

In this work we present a novel end-to-end framework for tracking and classifying a robot's surroundings in complex, dynamic and only partially observable real-world environments. The approach deploys a recurrent neural network to filter an input stream of raw laser measurements in order to directly infer object locations, along with their identity in both visible and occluded areas. To achieve this we first train the network using unsupervised Deep Tracking, a recently proposed theoretical framework for end-to-end space occupancy prediction. We show that by learning to track on a large amount of unsupervised data, the network creates a rich internal representation of its environment which we in turn exploit through the principle of inductive transfer of knowledge to perform the task of it's semantic classification. As a result, we show that only a small amount of labelled data suffices to steer the network towards mastering this additional task. Furthermore we propose a novel recurrent neural network architecture specifically tailored to tracking and semantic classification in real-world robotics applications. We demonstrate the tracking and classification performance of the method on real-world data collected at a busy road junction. Our evaluation shows that the proposed end-to-end framework compares favourably to a state-of-the-art, model-free tracking solution and that it outperforms a conventional one-shot training scheme for semantic classification

The Route Not Taken: Driver-Centric Estimation of Electric Vehicle Range

This paper addresses the challenge of efficiently and accurately predicting an electric vehicle's attainable range. Specifically, our approach accounts for a driver's generalised route preferences to provide up-to-date, personalised information based on estimates of the energy required to reach every possible destination in a map. We frame this task in the context of sequential decision making and show that energy consumption in reaching a particular destination can be formulated as policy evaluation in a Markov Decision Process. In particular, we exploit the properties of the model adopted for predicting likely energy consumption to every possible destination in a realistically sized map in real-time. The policy to be evaluated is learned and, over time, refined using Inverse Reinforcement Learning to provide for a life-long adaptive system. Our approach is evaluated using a publicly available dataset providing real trajectory data of 50 individuals spanning approximately 10,000 miles of travel. We show that by accounting for driver specific route preferences our system significantly reduces the relative error in energy prediction compared to more common, driver-agnostic heuristics such as shortest-path or shortest-time routes

Dietary supplementation with algae and polyphenols in rabbit male: Effects on semen quality traits

In recent years, many studies have been focused on natural substances that can affect health of animals. A mix of different extracts was used as dietary supplement and it consists exclusively of natural products. Its main components are polyphenols from terrestrial and marine origins and plant polysaccharides. The effect of this supplement on reproduction has not been reviewed in the past what is a reason why its effect on the reproduction potential of male rabbits was tested. The aim of the present study is to determine effects of natural mix during 120-days long in vivo experiment on selected reproductive traits of male rabbits. Natural mix was supplemented in two different concentrations (T1 - 0.3% and T2 - 0.6%) with the basal ingredients of the conventional rabbit feed in pellet form. In our experiments, emphasis was placed on both the spermatozoa concentration and its motility parameters as well as on the properties of seminal plasma and antioxidant activity. The dietary supplementation with the natural extracts mix positively altered the quality traits of rabbit spermatozoa, but these changes were statistically not significant. In experimental T1 group a significant increase of GPx and FRAP content, both regarding the antioxidant markers profile in seminal plasma was recorded. We can conclude that the supplementation of 0.3% of natural mix did not significantly negatively affect any of the studied reproductive parameters of male rabbits, but some improvement in several antioxidant parameters was found

Assessment of rabbit spermatozoa characteristics after amygdalin and apricot seeds exposure in vivo

This study evaluates rabbit spermatozoa motility parameters after in vivo administration of amygdalin and apricot seeds during a 28-day period. Apricot seeds are potentially useful in human nutrition and amygdalin is the major cyanogenic glycoside present therein. The rabbits were randomly divided into the five groups (Ctrl-Control, P1, P2, P3, P4) with 4 males in each group. Control group received no amygdalin/apricot seeds while the experimental groups P1 and P2 received a daily intramuscular injection of amygdalin at a dose 0.6 and 3.0 mg/kg b.w. respectively during 28 days. P3 and P4 received a daily dose 60 and 300 mg/kg b.w. of crushed apricot seeds mixed with feed during 28 days, respectively. CASA system was used to evaluate for motility, progressive motility, curvilinear velocity, amplitude of lateral head displacement and beat cross frequency. Intramuscular application of amygdalin resulted in a significant time- and dose-dependent decrease of spermatozoa motility as well as progressive motility. On the other hand, oral consumption of apricot seeds had no significant effect either on the rabbit spermatozoa motility or progressive motility over the entire course of the study. The analysis of the other motion characteristics revealed a similar trend depicting a continuous, time- and dose-dependent decrease of all parameters following intramuscular AMG administration, with significant differences particularly for the dose 3.0 mg AMG/kg b.w. On the other hand, oral administration of apricot seeds had no significant impact on spermatozoa motility parameters. The present study suggests that short-term intramuscular application of amygdalin decreased rabbit spermatozoa motility in vivo. Whereas, consumption of apricot seeds did not induce any change in rabbit spermatozoa in vivo. Our findings suggest dose-dependent negative effect of pure amygdalin, but not apricot seeds on the rabbit spermatozoa parameters. Keywords: Amygdalin, Apricot seeds, Spermatozoa, Rabbi