Overview / Usage

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.