Cartoon Network Game Jam

Cartoon Network Game Jam

Corey  Warning

Corey Warning

Portland, OR

A 48 hour game making competition to compliment the launch of "OK K.O.!" 200 developers, 45 games, 1 grand prize: complete your winning gam

Game Development, Networking

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Description

I co-organized a game jam where game makers created 48-hour jam entries based on judging criteria determined by Cartoon Network. At the end of the weekend, entries were given a short polishing period for their projects, then a winner was chosen! 16 Finalists won prize money and will have their work published in a Cartoon Network Game Jam bundle! The winning game jam team had a chance to negotiate a contract with Cartoon Network to provide the services to bring their project to fruition as a fully-fledged mobile app.

Links

CN Game Jam Website

Pump Up The Jam (Portland Mercury)

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Moloti N. created project Intelligent Home Security: Africa Motion Content encoder decoder using Deep Neural Networks

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Intelligent Home Security: Africa Motion Content encoder decoder using Deep Neural Networks

We propose the use of Drones to help communities enhance their security initiatives, to identify criminals during the day and at night. We use multiple sensors and computer vision algorithms to be able to recognize/detect motion and content in real-time, then automatically send messages to community members cell phones about the criminal activities. Hence, community members may be able to stop house breakings before they even occur.

Machine Intelligence Algorithm Design Methodology

AMCnet: https://github.com/AfricaMachineIntelligence/AMCnet https://devmesh.intel.com/projects/africa-motion-content-network-amcnet

We propose a deep neural network for the prediction of future frames in natural video sequences using CPU. To effectively handle complex evolution of pixels in videos, we propose to decompose the motion and content, two key components generating dynamics in videos. The model is built upon the Encoder-Decoder Convolutional Neural Network and Convolutional LSTM for pixel-level prediction, which independently capture the spatial layout of an image and the corresponding temporal dynamics. By independently modeling motion and content, predicting the next frame reduces to converting the extracted content features into the next frame content by the identified motion features, which simplifies the task of prediction. The model we aim to build should be end-to-end trainable over multiple time steps, and naturally learns to decompose motion and content without separate training. We evaluate the proposed network architecture on human AVA and UCF-101 datasets. We show state-of-the art performance in comparison to recent approaches. This is an end-to-end trainable network architecture running on the CPU with motion and content separation to model the spatio-temporal dynamics for pixel-level future prediction in natural videos.

// We then use this AMCnet pretrained model on the Video feed from the DJI Spark drone, integrated with the Movidius NCS to accelerate real-time object detection neural networks.

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Rohan S. updated status

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Rohan Sen

Recently I'm working virtual reality and android simultaneously, as I am intern in virtual and augmented reality related company so getting training in AR and VR

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Ngesa N. created project AndNotHotDog

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AndNotHotDog

Using IntelĀ® Optimization for Caffe to build a model that can be deployed in a mobile app that lets users snap a picture and then tells them whether it thinks an image is of a hotdog or not.

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