To investigate the causes of declining insect populations, a monitoring system is needed that automatically records insect activity and additional environmental factors over an extended period of time. For this reason, we use a sensor-based method with two event cameras. In this paper, we describe the system, the view volume that can be recorded with it, and a database used for insect detection.
Deep Event Visual Odometry sparsely tracks selected event patches over time. A key component of DEVO is a novel deep patch selection mechanism tailored to event data. We significantly decrease the pose tracking error on seven real-world benchmarks by up to 97% compared to event-only methods and often surpass or are close to stereo or inertial methods.
This paper focuses on five event-aided image and video enhancement tasks (i.e., event-based video reconstruction, event-aided high frame rate video reconstruction, image deblurring, image super-resolution, and high dynamic range image reconstruction), provides an analysis of the effects of different event properties, a real-captured and ground truth labeled benchmark dataset.
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Prophesee, inventor of the most advanced neuromorphic vision systems, will showcase new products with AMD, CIS, IDS, Lucid Vision Labs and the industry’s first automated defect detection demo together with Japanese expert Luci.