Presented by SK Reddy, Chief Product Officer AI at Hexagon Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ Detecting indoor human activity is used for security, patient care, baby monitoring, etc. purposes. Other than having another human being providing the service (i.e. a security guard, a nurse, baby’s mother, etc.), many solutions have been suggested using image processing neural networks that detect patient’s fall, baby walking, door open, etc. Many of these models have achieved higher prediction accuracy rates. But neural networks that use video cameras bring up privacy concerns. Custom made sensors, though solve the problem, are expensive. Researchers have proposed deep learning (DL) models use wifi signals to detect human activity. This is relatively recent research. I would like to discuss on how to design a DL to detect human activity to use Wifi signals that are available from off-the-shelf wifi routers. I will also discuss the architecture of such models, share the implementation problems and evaluate solutions that may address these problems.

2. Deep Learning
solutions for
Smart Cities
by SK Reddy
Chief Product Officer AI
linkedin.com/in/sk-reddy/

3. Which of the following are real?
Ref: PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION; http://research.nvidia.com/sites/default/files/publications/karras2017gan-paper-v2.pdf
1 2 3
4 5 6

4. Los Angeles’s Clean Streets
program
https://www.wsj.com/articles/the-rise-of-the-smart-city-1492395120

5. Listens to gun shot sounds…..

6. https://www.theguardian.com/sustainable-business/2017/feb/21/urban-heat-islands-cooling-things-down-with-trees-green-roads-and-fewer-cars
Thermal images in Melbourne

7. Smart City Use Cases
• Parking availability
• How long a bus will take?
• What is the right length for a stop light at every given minute
• Real time data about traffic and accidents
• Check if vehicles parked there have the right permits
• License plate recognition
• Detecting human action using videos
• Preventive maintenance of the road infrastructure
• Buses and trains communicate with each other and the general public
• Face recognition
• Find stolen cars
• Locate gunfire based on a sensor network
• Localized warnings about possible natural disaster
• Count vehicles and pedestrians
• Recognize faces
• Track the speed and movements of vehicles
• Count cars in a parking lot or track road use
• Emergency dispatch
• Ambulance call
https://www.techemergence.com/smart-city-artificial-intelligence-applications-trends/
Data
• Volume: terabytes (TB), petabytes
(PB), zettabytes (ZB)
• Variety: types of data (sensors,
devices, social networks, the web,
mobile phones, etc.)
• Velocity: how frequently the data is
generated (every millisecond, second,
minute, hour, day, week, month, or
year

8. Online data-processing architecture
https://arxiv.org/pdf/1709.01363.pdf
Main components of a Storm cluster
Apache Flink’s execution model

9. Data streaming
Infrastructure architecture
https://arxiv.org/pdf/1703.02755.pdf

10. https://arxiv.org/pdf/1712.01432.pdf
Large-scale video management

11. https://arxiv.org/pdf/1506.02640v5.pdf
Count cars in a parking lot
https://medium.com/the-downlinq/car-localization-and-counting-with-overhead-imagery-an-interactive-exploration-9d5a029a596b
YOLO2

12. https://arxiv.org/pdf/1704.08063.pdf
Comparison of open-set and closed-set face recognition
SphereFace: Deep Hypersphere Embedding for Face Recognition

13. https://arxiv.org/pdf/1704.00389.pdf
Analyzing videos of
human actions
TOP: MOTIONNET. BOTTOM: TRADITIONAL TEMPORAL STREAM. M IS THE NUMBER OF
ACTION CATEGORIES. STR: STRIDE. CH I/O: NUMBER OF CHANNELS OF INPUT/OUTPUT
FEATURES. IN/OUT RES: INPUT/OUTPUT RESOLUTION

14. https://arxiv.org/pdf/1706.07911.pdf
Large-Scale Mapping using Geo-Tagged Videos
Training MotionNet to learn optimal optical flow involves minimizing the following three objective functions:
1. A standard pixel-wise reconstruction error function
2. A smoothness loss to address the ambiguity of
estimating motion in non-textured regions (the
aperture problem)
3. A structural similarity (SSIM) loss that helps
MotionNet learn the structures of frames. It is
calculated as
The overall loss is a weighted sum of the pixel-wise
reconstruction loss, the pixel-wise smoothness loss and the
region-based SSIM loss
Spatial analysis of the 46th San Francisco Pride parade in 2016. Left: official
parade route. Right: map of classified videos. Note the correlation

15. Thank you
SK Reddy
www.linkedin.com/in/sk-reddy/