Not to mention debugging on real devices was extremely inefficient and painful.
Dealing with asynchronous processing also led us into a bunch of timing issues, which took the team quite some effort to address. We needed to implement some utilities to do things that sounded trivial but required significant effort to make it right and fast. We ran into several major design changes as some key design basics were overlooked. What came later were some tough challenges way above what we originally anticipated.
#Alfredcamera home security app android#
We started a small team to prototype on those goals first for the Android platform. Based on the goals, we had surveyed several open source projects that had the potential but we ended up using none of them as they either fell short on the features or were not providing the readiness/stabilities that we were looking for. At the beginning of the project, the goals were to create a new pipeline which should be 1) modular enough so we could swap core algorithms easily with minimal changes in other parts of the pipeline, 2) having GPU acceleration designed in place, 3) cross-platform as much as possible so there’s no need to create/maintain separate implementations for different platforms. In order to have a solid foundation to support our AI feature requirements for the coming years, we decided to rebuild our real-time video analysis pipeline. We had started building our AI features at Alfred Camera since 2017. This is important because the app is leveraging old phones and we'd like the feature to reach as many users as possible. The machine learning models for detection are hand-crafted and trained by our team using TensorFlow, and run on TensorFlow Lite with good performance even on mid-tier devices. Once it identifies a moving object in the area, the app will begin recording the video and send notifications to the device owner.
Our aim is to integrate AI technology into devices that are accessible to everyone.Īlfred Camera currently has a feature called Moving Object Detection, which continuously uses the device’s camera to monitor a target scene. The Alfred Camera team is composed of professionals in various fields, including an engineering team with several machine learning and computer vision experts. The mission of Alfred Camera is to provide affordable home security so that everyone can find peace of mind in this busy world. By downloading the app, users are able to turn their spare phones into security cameras and monitors directly, which allows them to watch their homes, shops, pets anytime. What is Alfred Camera?Īlfred Camera is a smart home app for both Android and iOS devices, with over 15 million downloads worldwide. In this article, we’d like to give you a short overview of Alfred Camera and our experience of using MediaPipe to transform our moving object feature, and how MediaPipe has helped to get things easier to achieve our goals. Please note that the information, uses, and applications expressed in the below post are solely those of our guest author, Alfred Camera. Guest post by the Engineering team at Alfred Camera
0 kommentar(er)
