Test Process Automation for Noisy Car Datasets

Test Process Automation for Noisy Car Datasets

senthamarai kannan subramanian

senthamarai kannan subramanian

Bengaluru, Karnataka

A Car Company has to Test a Number of Spare Components for a Car Model! Usually Car Datasets has a Number of Noisy Components !

Artificial Intelligence

Description

A Car Company Providing a Number of Model Options has to Carry out a Number of Tests on the Spare Components of a Car which can add Noise or Air Pollution! An Intelligent Test Automation Software could reduce this Number of Tests for a Specific Car Model by Automating the Test Process!

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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

Medium 0 3au0 jy vjn n61ystd8g dyro8yb3k4kgyt0c0rzoclbqcwaf7yuqykuanln tc23mhtnyjwyqprilflkv5dnftwyqxr7zwlkvhsuytwy8prg8ksx7xyjf94clzii6n8vrr

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 Intel® 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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