TASS Autonomous Sight System

The TASS Network is made up of IoT connected video cameras and a local hub which homes an IoT connected Convolutional Neural Network which processes real time video frames to detect known people or intruders.

Artificial Intelligence IoT Network Progress

333
TASS Autonomous Sight System

Demonstrating recent upgrades to the Artificially Intelligent IoT Network, including TASS A.I. CCTV Hub, autonomous IoT device / A.I. / human triggered communication and first preview of the IntelliLan IoT Android App. #InternetOfThings #IoT #AI #ArtificialIntelligence #IntelSoftwareInnovators #TechBubbleTechnologies

  

TASS A.I. Server POC Demo

320
TASS Autonomous Sight System

A quick demonstration of an basic use case for the TASS A.I. Artificial Intelligence CCTV / Smart home network.

  

An introduction to Project H.E.R.

287
TASS Autonomous Sight System

H.E.R. is an innovative, highly integrated solution which evolves the Smart Home into an Intelligent one. H.E.R. can recognize and identify a dependant family member and help monitor and adjust the environment to provide a safer home for families with dependant family members.

  

Project H.E.R wins Intel Experts Award

314
TASS Autonomous Sight System

The moment Intel Experts announced that Project H.E.R had been selected for the Intel Experts Award.

  

Project H.E.R Intel / Microsoft / IoT World Solutions Hackathon Presentation

295
TASS Autonomous Sight System

Presenting the presentation for Project H.E.R (HomeCare Embedded Recognition) at the Intel / Microsoft / IoT Solutions World Congress Hackathon in Barcelona. Project H.E.R is a Neural Network for computer vision built on a Intel Joule and a range of smart home sensors communicating via the Intel Nuc using the TechBubble Technologies IoT JumpWay MQTT libraries and Microsoft Azure for serving the applications. For this project our team won the Intel Experts Award and now have a letter of intent from Sabadell City Hall to further the project and in the early days of working towards the Intel Enabling IoT Program.

  

Controlling IoT lights with TOA the Artificial Intelligence & TechBubble IoT JumpWay (2016)

257
TASS Autonomous Sight System

Controlling IoT lights with TOA the Artificial Intelligence & TechBubble IoT JumpWay (2016)

  

Inception-V3 transfer learning on an RPI 3, 100% facial identification rate!

268
TASS Autonomous Sight System

So previously we had built TASS on a Raspberry Pi using purely OpenCV, a one layer neural network that uses haarcascades. Unhappy with the identification rate and time the prediction took, I looked into using Tensorflow with help of TechBubble team member Andrej Peteling. In particular we were interested in transfer learning to train the final layer of the Inception V3 model (paper here: https://arxiv.org/abs/1512.00567 ). After a few issues with training, we managed to successfully train the final layer of the Inception V3 model directly on a Raspberry Pi 3, and now have 100% identification rate at almost 95% increase in speed compared to our previous setup. 1, seen in the video, represents my TechBubble Technologies user id, which is in place to uphold privacy of any identified person until the data gets to our server. As of yet, I have not found documentation online of anyone accomplishing training the layer on a Raspberry Pi, so we are very happy with the outcome, and even more happy with the improvement in prediction time and overall accuracy of the identification. After this video was created we stumbled upon some issues which are documented here: https://www.techbubble.info/blog/artificial-intelligence/machine-learning/entry/artificial-intelligence-machine-learning-internet-of-things EARLY STAGE TRAINING LOG: 2016-12-24 02:14:53.428681: Step 0: Train accuracy = 38.0% 2016-12-24 02:14:53.428948: Step 0: Cross entropy = 1.224129 2016-12-24 02:14:53.915460: Step 0: Validation accuracy = 25.0% 2016-12-24 02:14:58.495277: Step 10: Train accuracy = 97.0% 2016-12-24 02:14:58.495489: Step 10: Cross entropy = 0.586082 2016-12-24 02:14:58.930113: Step 10: Validation accuracy = 94.0% 2016-12-24 02:15:03.473702: Step 20: Train accuracy = 100.0% 2016-12-24 02:15:03.473912: Step 20: Cross entropy = 0.406726 2016-12-24 02:15:03.907996: Step 20: Validation accuracy = 100.0% 2016-12-24 02:15:08.411214: Step 30: Train accuracy = 100.0% 2016-12-24 02:15:08.411426: Step 30: Cross entropy = 0.294103 2016-12-24 02:15:08.843722: Step 30: Validation accuracy = 100.0% 2016-12-24 02:15:13.364921: Step 40: Train accuracy = 100.0% 2016-12-24 02:15:13.365132: Step 40: Cross entropy = 0.186366 2016-12-24 02:15:13.802341: Step 40: Validation accuracy = 100.0% 2016-12-24 02:15:18.338873: Step 50: Train accuracy = 100.0% 2016-12-24 02:15:18.339084: Step 50: Cross entropy = 0.206962 2016-12-24 02:15:18.773215: Step 50: Validation accuracy = 100.0% 2016-12-24 02:15:23.290912: Step 60: Train accuracy = 100.0% 2016-12-24 02:15:23.291127: Step 60: Cross entropy = 0.176654 2016-12-24 02:15:23.726591: Step 60: Validation accuracy = 100.0% 2016-12-24 02:15:28.352231: Step 70: Train accuracy = 100.0% 2016-12-24 02:15:28.352446: Step 70: Cross entropy = 0.137122 2016-12-24 02:15:28.786593: Step 70: Validation accuracy = 100.0% 2016-12-24 02:15:33.313190: Step 80: Train accuracy = 100.0% 2016-12-24 02:15:33.313766: Step 80: Cross entropy = 0.110478 2016-12-24 02:15:33.747310: Step 80: Validation accuracy = 100.0% 2016-12-24 02:15:38.284064: Step 90: Train accuracy = 100.0% 2016-12-24 02:15:38.284277: Step 90: Cross entropy = 0.141551 2016-12-24 02:15:38.718164: Step 90: Validation accuracy = 100.0% 2016-12-24 02:15:43.239743: Step 100: Train accuracy = 100.0% 2016-12-24 02:15:43.239955: Step 100: Cross entropy = 0.094765 2016-12-24 02:15:43.671750: Step 100: Validation accuracy = 100.0% 2016-12-24 02:15:48.178953: Step 110: Train accuracy = 100.0% 2016-12-24 02:15:48.179167: Step 110: Cross entropy = 0.101152 2016-12-24 02:15:48.614145: Step 110: Validation accuracy = 100.0% 2016-12-24 02:15:53.188334: Step 120: Train accuracy = 100.0% 2016-12-24 02:15:53.188543: Step 120: Cross entropy = 0.083642 2016-12-24 02:15:53.621917: Step 120: Validation accuracy = 100.0% ....continued accuracy up until 4000 steps. FINAL OUTCOME OF TRAINING: Final test accuracy = 100.0% PREDICTION ACCURACY TO DATE: 100% identification!!!! Average confidence 0.998

  

A technical introduction to Project H.E.R.

239
TASS Autonomous Sight System

A brief video introducing the technical features of Project H.E.R.

  

A.I. Raspberry Pi video camera and autonomous IoT communication

325
TASS Autonomous Sight System

A.I. Raspberry Pi video camera and autonomous IoT communication

  

LinGalileo Security System

270
TASS Autonomous Sight System

LinGalileo Security System

  

LinGalileo Security System Control Panel, TechBubble GUI

278
TASS Autonomous Sight System

LinGalileo Security System Control Panel, TechBubble GUI

  

TASS TechBubble Assisted Security System now has voice synthesis, voice recognition and CCTV (2015)

269
TASS Autonomous Sight System

TASS TechBubble Assisted Security System now has voice synthesis, voice recognition and CCTV (2015)