Recent Advances in GPU-Based Intelligent Video...

GTC'16, San Jose, USA

April 5, 2016

Recent Advances in GPU-Based Intelligent

Video Analysis

Beijing Vion Technology Inc.

Hai Tao, Dr., CEO

Company Introdution

• The Beijing Vion Technology, Inc.(BVT) was founded in 2005,

specialized in computer vision technology. The company currently

employs around 200 talented staff members.

• The company has developed products in several markets including

intelligent transportation systems (ITS),business intelligence

systems, security and urban management systems. These products are

deployed broadly across China and BVT is recognized as one of the

leading computer vision companies in China.

• Recently, BVT has developed core algorithms for ADAS and UBI

products.

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A Typical IVA System

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Analog Cam

Concealed Camera

Half Dome Cam

IP Cam

IVA Box

Network

Switch

PC Client

Data Manage

ment

Mobile App

数据中心

TK1-Based IVA Box - VT-B2081A (CBOX)

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TK1-Based IVA Box - VT-B2082A (IBOX)

2 TK1 GPU's

8 analog video inputs

8 audio inputs

2 RJ45 1000M ethernet ports

built-in GPS

1 2.5" hard drive

Alarm: 4 in, 2 out

1 RS232 and 1 RS485

SPF connection

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DEMO: IVA Algorithms Running on CBOX/IBox

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• People counting - Shopping mall

• People counting - Summer Palace

• People counting - Bus

• People counting - Subway

• People counting - Cinema

• Urban management - Vending

• Law enforcement - Fighting detection

• Law enforcement - Parking violation

A GPU-Based Smart Camera - VT-E412

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A 12MP@30fps GPU smart camera

The 1st GPU-based smart camera that can reach 12MP @30 fps

Applications: Face recognition, gender and age recognition, people counting, LPR, fight and chasing detection, premiter monitoring

Main features

12 MP CMOS sensor @ 30 fps

FPGA+TK1 ISP pipeline

Full resolution video analytics

Sensors: gyroscope, accelerometer, digital compass

GPS & Wifi/3G/4G supported

Audio: 1 in, 1 out

Storage: 2 SD cards

Alarm: 2 in, 1 out, 1 RS232 and 1 RS485

DEMO: IVA Algorithms Running on VT-E412

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• Facial detection

• Gender and age recognition

• Vehicle detection, tracking and recognition

StartNet GPU Cluster

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40 GPUs in a 2U Rack Server

Characteristics of backend IVA

Handreds of h.264/HEVC video streams from IPCs

The computation need for decoding and vision algorithm roughly in 1:1 to 1:10 range

Using traditional server: not enough matching HW decoding power, higher FLOPS/Watt, Lower computing density (FLOPS/SpaceUnit)

Many-GPU solution

TK1/TX1 balances the HW video decoding/encoding capabilities with computing power

Lower FLOPS/Watt

Much higher computing density

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Vehicle barnd/model and color recognition

Frontal and back view of vehicles

Day/night time

Average 90% accuracy for vehicle brand recognition

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Driver behavior recognition

Not fastening seatbelt

Using cell phones

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Prototype ADAS Algorithm Framework

Deep neural network based object detection and tracking (vehicles, pedestrians, bicycles, etc)

Deep neural network based road scene understanding (traffic lane detection and following)

License plate recognition

ADAS main functions including lane departure warning (LDW),vehicle distance monitoing warning (HMW), pedestrian collision warning (PCW), and driving behavior analysis (DBA)

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Prototype ADAS Algorithm Framework

Demonstration

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Thank you!

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