# US 2021/0174166 A1 · Real-time prediction program for change in occupants in large exhibition hall based on deep learning

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- Number: US 2021/0174166 A1
- Document ID: US20210174166A1
- Status: Published application
- Kind: A1
- Application number: 17/092,782
- Filed: 2020-11-09
- Published: 2021-06-10
- Earliest claimed: 2019-12-10
- Assignees: Pusan National University Industry-University Cooperation Foundation
- Applicants: Pusan National University Industry-University Cooperation Foundation
- Inventors: Giltae Song; Seonghyeon Kim
- Web page: https://patentgrep.com/patent/US20210174166A1

## CPC

- G06N 3/08: Learning methods
- G06F 18/10: Pre-processing; Data cleansing
- G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting
- G06F 18/217: {Validation; Performance evaluation; Active pattern learning techniques}
- G06N 3/044: Recurrent networks, e.g. Hopfield networks
- G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
- G06N 3/09: Supervised learning
- G06V 10/764: using classification, e.g. of video objects
- G06V 10/774: Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
- G06V 10/776: Validation; Performance evaluation
- G06V 10/82: using neural networks
- G06V 20/36: {Indoor scenes}
- G06V 20/52: Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

Disclosed are a method and apparatus for predicting a change in the occupants within a large exhibition hall in real time based on deep learning. A proposed method of predicting a change in the number of occupants within a space in real time includes dividing, into zones, a space where a number of occupants is to be predicted and pre-processing data related to a number of occupants within the space collected through simulations, generating the pre-processed data in a form of time-series data for deep learning, training a deep learning model for predicting a number of occupants in each divided zone using the generated time-series data, and predicting the number of occupants within the space by inputting, to the trained model, the data related to a number of occupants within the space collected in real time through socket communication with a server.

## Claims

### Claim 1

A method of predicting a change in the number of occupants within a space in real time, the method comprising:
dividing, into zones, a space where a number of occupants is to be predicted and pre-processing data related to a number of occupants within the space collected through simulations;
generating the pre-processed data in a form of time-series data for deep learning;
training a deep learning model for predicting a number of occupants in each divided zone using the generated time-series data; and
predicting the number of occupants within the space by inputting, to the trained model, the data related to a number of occupants within the space collected in real time through socket communication with a server.

### Claim 2 (depends on 1)

The method of claim 1, wherein dividing, into zones, a space where a number of occupants is to be predicted and pre-processing data related to a number of occupants within the space collected through simulations comprises:
measuring a share of visitors to each zone using image sensors and counting devices installed at given intervals, and
reinforcing the data through simulations in order to train the deep learning model.

### Claim 3 (depends on 2)

The method of claim 2, wherein:
the share of the visitors to each divided zone is measured for a predetermined time interval and a predetermined time, and
all of data sets are normalized within a range of 0 to 1 using a zero-padding scheme and minimum-maximum normalization.

### Claim 4 (depends on 1)

The method of claim 1, wherein generating the pre-processed data in a form of time-series data for deep learning comprises re-configuring the pre-processed data based on the deep learning model using a long-short term memory (LSTM).

### Claim 5 (depends on 1)

The method of claim 1, wherein training a deep learning model for predicting a number of occupants in each divided zone using the generated time-series data comprises using an individual model performing a share prediction model for receiving the time-series data based on an LSTM and outputting one occupancy number in one zone on a specific time.

### Claim 6 (depends on 5)

The method of claim 5, wherein a prediction of a share is indicated as a weighted sum of shares for a plurality of divided zones on a current time.

### Claim 7

An apparatus for predicting a change in a number of occupants within a space in real time, the apparatus comprising:
a pre-processor configured to pre-process data related to a number of occupants within a space collected through simulations;
a time-series data generator configured to generate the pre-processed data in a form of time-series data for deep learning;
a training unit configured to divide, into zones, a space where a number of occupants is to be predicted and to train a deep learning model for predicting a number of occupants in each divided zone using the generated time-series data; and
a prediction unit configured to predict the number of occupants within the space by inputting, to the trained model, the data related to a number of occupants within the space collected in real time through socket communication with a server.

### Claim 8 (depends on 7)

The apparatus of claim 7, wherein the pre-processor
measures a share of visitors to each zone using image sensors and counting devices installed at given intervals, and
reinforces the data through simulations in order to train the deep learning model.

### Claim 9 (depends on 8)

The apparatus of claim 8, wherein:
the share of the visitors to each divided zone is measured for a predetermined time interval and a predetermined time, and
all of data sets are normalized within a range of 0 to 1 using a zero-padding scheme and minimum-maximum normalization.

### Claim 10 (depends on 7)

The apparatus of claim 7, wherein the time-series data generator re-configures the pre-processed data based on the deep learning model using a long-short term memory (LSTM).

### Claim 11 (depends on 7)

The apparatus of claim 7, wherein the training unit uses an individual model performing a share prediction model for receiving the time-series data based on an LSTM and outputting one occupancy number in one zone on a specific time.

### Claim 12 (depends on 11)

The apparatus of claim 11, wherein a prediction of a share is indicated as a weighted sum of shares for a plurality of divided zones on a current time.