We conducted the catch prediction for juvenile bluefin tuna in the water close to Kagoshima based on the support vector regression, a kind of statistical machine learning, using catch of juvenile bluefin tuna in other prefectures, skipjack in Kagoshima, marine environment in fishing place like sea surface temperature as input variables. There is a few difference of predicted value in 2011 based on the support vector regression and the corresponding observed one. Prediction accuracy of the model was high. As a result of attribution analysis by using the support vector regression, the influence of catch prediction in other prefectures was wholly larger than that of the factor of marine environmental, especially the impact on catch prediction for juvenile bluefin tuna in Okinawa prefecture was large.
In this paper, a vehicle detection framework for road surveillance systems is presented. The framework is based on co-occurrence histograms of oriented gradients (CoHOGs), which are effective for object detection, using the ViscontiTM 2 high-performance image recognition processor. The authors have developed vehicle detection and tracking technologies on the platform and confirmed these technologies can be applied to road surveillance systems.
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