158,787 research outputs found

    Vision applications in agriculture

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    From early beginnings in work on the visual guidance of tractors, the National Centre for Engineering in Agriculture has built up a portfolio of projects in which machine vision plays a prominent part. This presentation traces the history of this research, including some highly unusual topics

    Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets

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    In this work, we explore the correlation between people trajectories and their head orientations. We argue that people trajectory and head pose forecasting can be modelled as a joint problem. Recent approaches on trajectory forecasting leverage short-term trajectories (aka tracklets) of pedestrians to predict their future paths. In addition, sociological cues, such as expected destination or pedestrian interaction, are often combined with tracklets. In this paper, we propose MiXing-LSTM (MX-LSTM) to capture the interplay between positions and head orientations (vislets) thanks to a joint unconstrained optimization of full covariance matrices during the LSTM backpropagation. We additionally exploit the head orientations as a proxy for the visual attention, when modeling social interactions. MX-LSTM predicts future pedestrians location and head pose, increasing the standard capabilities of the current approaches on long-term trajectory forecasting. Compared to the state-of-the-art, our approach shows better performances on an extensive set of public benchmarks. MX-LSTM is particularly effective when people move slowly, i.e. the most challenging scenario for all other models. The proposed approach also allows for accurate predictions on a longer time horizon.Comment: Accepted at IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2019. arXiv admin note: text overlap with arXiv:1805.0065

    Automated soil hardness testing machine

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    This paper describes the design and performance of a mechatronic system for controlling a standard drop-hammer mechanism that is commonly used in performing outdoor soil or ground hardness tests. A low-cost microcontroller is used to control a hydraulic actuator to repeatedly lift and drop a standard free-falling weight that strikes a pipe (sampler) which is pushed deeper into the ground with each impact. The depth of the sampler pipe and position of the hydraulic cylinder are constantly monitored and the number of drops, soil penetration data and other variables are recorded in a database for future analysis. This device, known as the “EVH Trip Hammer”, allows the full automation and faster completion of what is typically a very labour-intensive and slow testing process that can involve human error and the risk of human injuries

    The use of machine vision for assessment of fodder quality

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    At present fodder is assessed subjectively. The evaluation depends greatly on a personal opinion and there can be large variations in assessments. The project has investigated the use of machine vision in several ways, to provide measures of fodder quality that will be ojective and independent of the assessor. Growers will be able to quote a quality measure that buyers can trust. The research includes the possibility of discerning colour differences that are beyond the capability of the human eye, while still using equipment that is of relatively modest cost

    A high speed Tri-Vision system for automotive applications

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    Purpose: Cameras are excellent ways of non-invasively monitoring the interior and exterior of vehicles. In particular, high speed stereovision and multivision systems are important for transport applications such as driver eye tracking or collision avoidance. This paper addresses the synchronisation problem which arises when multivision camera systems are used to capture the high speed motion common in such applications. Methods: An experimental, high-speed tri-vision camera system intended for real-time driver eye-blink and saccade measurement was designed, developed, implemented and tested using prototype, ultra-high dynamic range, automotive-grade image sensors specifically developed by E2V (formerly Atmel) Grenoble SA as part of the European FP6 project – sensation (advanced sensor development for attention stress, vigilance and sleep/wakefulness monitoring). Results : The developed system can sustain frame rates of 59.8 Hz at the full stereovision resolution of 1280 × 480 but this can reach 750 Hz when a 10 k pixel Region of Interest (ROI) is used, with a maximum global shutter speed of 1/48000 s and a shutter efficiency of 99.7%. The data can be reliably transmitted uncompressed over standard copper Camera-Link® cables over 5 metres. The synchronisation error between the left and right stereo images is less than 100 ps and this has been verified both electrically and optically. Synchronisation is automatically established at boot-up and maintained during resolution changes. A third camera in the set can be configured independently. The dynamic range of the 10bit sensors exceeds 123 dB with a spectral sensitivity extending well into the infra-red range. Conclusion: The system was subjected to a comprehensive testing protocol, which confirms that the salient requirements for the driver monitoring application are adequately met and in some respects, exceeded. The synchronisation technique presented may also benefit several other automotive stereovision applications including near and far-field obstacle detection and collision avoidance, road condition monitoring and others.Partially funded by the EU FP6 through the IST-507231 SENSATION project.peer-reviewe

    Bovine intelligence for training horses

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    A rail-mounted model of a small cow is to be used in the training of horses for camp-drafting contests. The paper concerns the addition of sensors and a strategy to enable the machine to respond to the proximity of the horse in a manner that will represent the behaviour of a live calf
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