This is a presentation I shared in Practically AI on 1 Mar 2018. I shared the architectural details of our Smart City platform and also some examples of prominent architectures that are used by organizations. I also discussed some specific implementation details of Smart City uses cases like Face Re-Id, Vehicle Re-Id, Detecting and Counting Cars in a parking lot, etc.

2. Smart Cities & AI
by SK Reddy
Chief Product Officer AI & ML
linkedin.com/in/sk-reddy/
Digitalist (www.digitalist.global)
Practically.ai

3. http://www.un.org/en/development/desa/population/publications/pdf/urbanization/the_worlds_cities_in_2016_data_booklet.pdf
World cities in 2030
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4. Perceived benefits of smart device purchase
https://www.pwc.com/us/en/advisory-services/publications/consumer-intelligence-series/smarthome.html
Smart homes and smart devices Practically.ai
Motivators for device adoption
• savings
• safety
• convenience
• control

5. ET Brain (Alibaba Cloud’s ultra-intelligent AI Platform); https://www.alibabacloud.com/et
Application Scenarios
Practically.ai

6. 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
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7. Online data-processing architecture
https://arxiv.org/pdf/1709.01363.pdf
Main components of a Storm cluster
Apache Flink’s execution model
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8. Text processing Framework
https://arxiv.org/pdf/1709.03406.pdf
• Lowercasing
• Lemmatization
• Tokenization
• Transforming repeated characters: (e.g. "loooool" to "loool”)
• Punctuation removal
• Cleaning Entities and Numerical Symbols
• Removing URLs, user mentions, hashtags and digits from the
text messages
• Stop and short words removal
• Geographic Distributions
• Temporal Frequencies
• volume of tweets posted per hour, per day, as well as the
activity by day-of-the-week or hour-of-the-day
• Content Composition
• metadata (hashtags, user mentions, URLs and media
attached) exploration for added-value information
Practically.ai

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

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

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
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12. Radio based approach for traffic flow detection
https://arxiv.org/pdf/1801.03317.pdf
Example ray tracing scenario illustrating the shadowing
effects caused by a SUV passing the proposed classification
system
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13. https://arxiv.org/pdf/1704.08063.pdf
Comparison of open-set and closed-set face recognition
Practically.ai
SphereFace: Deep Hypersphere Embedding for Face Recognition

14. 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
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15. 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
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16. Thank you
SK Reddy
www.linkedin.com/in/sk-reddy/
Practically.ai