Computer Vision for Automatic Traffic Monitoring

March 2018

Group of Prof. Yehoshua Y. Zeevi

2

Agenda

• Context

• Goals / Vision

• Technologies / Infrastructure

• Proposed solution

• Challenges

• Results

• Conclusions and Recommendations

• Further work

Context - Smart City

Smart City

Smart Infrastructure

Traffic Control

Safety Efficiency Modularity Security

Context - Smart City

5

Focus – Pedestrian Monitoring

Demo

• Traffic monitoring / Junction overview

• Vehicle monitoring

• Pedestrian monitoring

• Pedestrian safety

• …

7

Goals

8

Possible Technologies

Sensors: motion sensors, magnetic sensors, cameras, BT, LIDARs…

Connectivity: wired / wireless, PTP, IoT, cloud services, …

Computing: low cost/energy computers, PCs, GPUs, cloud computing, …

Computer Vision: simple algorithms, advanced algorithms, deep learning

Haifa Municipal Traffic Control Center Cameras

Raspberry Pi board with HD camera (~100$)

ODROID (~200$)

NVIDIA Jetson GPU (~600$)

INTEL NUC (100 – 500 $)

9

Infra Structure

Simple• Spatial Operators• Time OperatorsAdvanced• Machine Learning

• Object Detection• Object Recognition• Object Tracking• Scene Understanding

10

Computer Vision Tools

11

Control center

Proposed Solution

• IoT (Internet of Things) Methodology

להחליף לרמזור הולכי רגל

12

Golda junction – prior data

Lane recognition

Vehicle counting per lane

Starting PositionVehicle traffic monitoring

13

Chorev junction

Vehicle traffic monitoring

Main FocusPedestrian Monitoring

• Pedestrian counting

• Average waiting time

• (abnormal behavior detection)

Spatial & Time Operators

Person No Person

Machine learning

* Videos from Haifa Traffic Control Center

Pedestrian counting

Pedestrian monitoring

* Videos from Haifa Traffic Control Center

Pedestrian monitoring algorithm

* Videos from Haifa Traffic Control Center

Pedestrian Counting

• New video data• Taken with Raspberry Pi camera

at Haifa Ziv junction

Some statistics

Person People Empty

TPR(%) PPV(%) ACC(%) TPR(%) PPV(%) ACC(%) TPR(%) PPV(%) ACC(%)

Nesher1b 83 85 96 87 94 97 98 97 97

Nesher4a 98 92 99 100 94 99 98 99 98

Ziv1a 98 93 97 98 95 98 95 97 96

TPR(%) PPV(%) ACC(%)

Average Std Average Std Average Std

Person 93 9 90 4 97 2

People 95 7 94 1 98 1

Empty 97 2 98 1 97 1

Challenges

• Sensitivity to viewing angles

20

Control center

IoT Cloud Services

• IoT (Internet of Things) Methodology

Chorev Junction Mapping 1

cam01

cam02

cam03

cam04

cam05

cam06

cam02 cam04

cam06

cam07

cam08

cam09

cam10

cam07

cam08cam10

cam09

Chorev Junction Mapping 2

Junction Diagram

M1

M2

M3M4

M5

M6

M7

M8

E1

E2

E3

E4

E5

E6

E7

E8

E9

Streamed Information

Control center

No images

No infringement of privacy

Results

26

Control center

Into the Future

• IoT (Internet of Things) Methodology

Smart Traffic Lights Standardization

Use of IoT

Conclusions

• Standardization: Traffic lights with built-in cameras

• Distributed computation• Low cost smart sensors• Connectivity through cloud services• Availability to nearby vehicles• Identification of behaviour with reference to age

Current Activity

Tel Aviv

Traffic Light Control - Habima

Disabled Pedestrian Monitoring

The Team

Principal Investigator:

Prof. Y. Zeevi

Researchers :

Dr. Eli Appleboim, Dr. Israel Berger, Johanan Erez, Dr. Rami Cohen, Roy Miterani, Aviad Levis

Vision and Image Sciences Lab :

Daniel Yagodin, Ina Talmon, Aviel Avraham, Alon David,

Zvi Lederer, Ben Ajami

Networked Software Systems Lab:

Roy Miterani, Hovav Gazit

~15 students:

Tom Shitrit, Yan Yampolsky, Lior Haimovich, Roi Sinoff, Tali Srebo, Ohad Spitzer, Alon Mamistavlov, Yonatan Shlain, Yonatan Shahor, Amit Enoch, Amit Gacket, Emanual Alkobi, Nofar Mann, Alon Zabatani, Morag Tohamy

Technion Transportation Institute

Dr. Ayellet Gal-Zur, Oded Komar, Iliah Finkelberg

Haifa Traffic Control Center

Anat Gilad, Sharona Cohen

30

Field Tests

31

Field Tests