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TraVis: Web-Based Vehicle Counter with Traffic Congestion Estimation Using Computer VisionPresented by: Aguirre, Byron Franco Alcantara, Jan Andre Trinidad, John Ferdinand

Thesis adviser: Dr. Joel P. Ilao

Hello Goodmorning panelistsWe are the group Travis and our Thesis is a Web-based vehicle counter with traffic congestion estimation using computer vision.I am,(names)And we are advised by Dr. Joel Ilao.2


This is the flow of the presentation. (wait few seconds)

Problem Statement

So for Problem that we would like to tackle,


Problem StatementIn the Philippines, many traffic cameras are being installed where recorded traffic videos are monitored by traffic personnel. However, these cameras might have been recording videos 24/7 but the people who monitor them might not be watching. The unmonitored videos then become unused since a traffic situation has already occurred in an area. With this, a traffic monitoring system that makes use of all recorded videos can provide information to the public regarding the traffic situation in an area.

Nowadays, there are many traffic cameras being installed to monitor traffic. However, not all of them might have people watching all the time. These unmonitored videos become unused since the traffic situation changes in time. With that, a traffic monitoring system that makes use of all the recorded videos can provide information to the public regarding traffic situation.5


Here are our objectives to guide us in this project6

General ObjectiveThe aim of this study is to develop a vision-based system for counting vehicles and estimating traffic congestion levels in road sections installed with traffic surveillance cameras, accessible through a web interface.

For our general objective,

we aim to develop a vision-based system for counting vehicles and estimating traffic congestion levels that would be accessible through a web interface.7

Specific Objectivesgather traffic videos taken by roadside traffic surveillance cameras;develop machine vision algorithms for processing traffic videos that can:classify vehicles according to type;track and count the number of vehicles seen;

To develop our system, we came up with these specific objectives. First off, we have to gather traffic videos. Next would be the development of the machine vision algorithms for the processing the videos. These algorithms would classify vehicles according to type, count the number of vehicles and also track them.8

Specific Objectives (cont.)generate graphs of traffic congestion levels based on vehicle count statistics estimated from traffic videos;design a suitable database for efficiently storing traffic surveillance videos and corresponding traffic statistics; and develop an interactive web interface for accessing relevant data and information from traffic surveillance videos;

After that, we generate graphs of traffic congestion levels based on the data made from the algorithms. To store these traffic statistics, we will design a suitable database and to show these results, we will develop an interactive web interface.9

Project Scope and LimitationThis study will count and classify vehicles seen in a traffic surveillance cameras Field Of View via application of computer vision techniques. The system that will be developed can:identify and classify vehicles

distinguish adjacent vehicles

The scope of TraVis include:Ideitification and classification of vehiclesDistinguishin adjacent vehicles in traffic10

Project Scope and Limitation (cont.)estimate traffic congestion levels based on vehicle counts

allow users to view results using an intuitive web interface

Estimate traffic congestion levels based on vehicle countsShow the results to the users through a web application11

Project Scope and Limitation (cont.)no hardware implementation of video acquisition

use traffic videos from Archers Eye

A limitation of our study is that we have no hardware implementation such as in video acquisition. Our traffic videos were acquired from Dlsus Archers Eye.12

Project Scope and Limitation (cont.)Factors affecting the quality of counting:low quality videosslow frame rates variations in lighting occlusion

We also considered the following that may affect the performance of our system.13

Project Scope and Limitation (cont.)Factors that are not consideredTraffic at nightSwerving vehicles

Traffic at night and swerving vehicles were not considered14

Project Scope and Limitation (cont.)IP Cameras were usedVideos obtained through ITSVideo file: .mp4 at 6 FPS

The IP cameras recorded videos at 6 FPS only, adding challenges when being processed for vehicle detection.15

Training DataDateTimeAndrew Hall ViewAugust 25, 20140700-1800August 26, 20140700-1800North Gate View (Physics Lab videos)May 23, 20140600-1300

The following is the training data used for the purpose of testing the system.16

Project Scope and Limitation (cont.)Can detect:

Small VehiclesMedium VehiclesLarge VehiclesCarSUVTruckSedanJeepBusVan

In TraVis, vehicle classification is done through the following categories.

(The types of vehicles are divided to the following. Small, Medium and Large. You can see in the table the distribution of the types of vehicles.)17

Project Scope and Limitation (cont.)Performance assessed by:AccuracyAgainst:OcclusionNumber of vehicles present

The performance of the system is assessed by the accuracy against occlusion and the number of vehicles present


System Overview

This is a diagram of our system overview. The users selects a video to process through the web application. The system then selects the videos to process and have their status inserted into the database. After generating the traffic statistics by Matlab instances, the data is stored again in the database. These data would be continually uploaded to the web application so as to have the feel of semi-real time.19

System Implementation

Our system implementation is divided into three modules: Video Acquisition, Vehicle Detection and Statistics Generation.20

Architectural Design

Our system implementation is divided into three modules: Video Acquisition, Vehicle Detection and Statistics Generation.


Architectural DesignVideo AcquisitionVehicle DetectionStatistics Generation

First is Video Acquisition..22

Video Acquisition

This shows the process of the Video Acquisition module. It starts with an input traffic video, followed by getting the frames before they are processed.

(For our Video Acquisition module, this is a summary of the process of it.)23

Video Acquisition Flowchart

In the video acquisition module, user selects a video, travis then checks if the video is available, if not flag the database whether it was processed already (in the past) or not. If not yet processed, it will invoke a Matlab instance in the backend of the system. The selected video would be processed. During the processing, it will check (frequently) if the video is finished or not. If it is, the results are displayed and if not, check if 5 minutes in video time, has already passed and then insert the data in the database. After that return to processing the video until finished.24

Architectural DesignVideo AcquisitionVideo InputFrame ExtractionVehicle DetectionStatistics Generation

This will cover further discussions of the sub-modules in the video acquisition. Starting with the video input sub-module25

Video InputThe videos are stored locally and accessed by the server via the URL passed when the used has chosen a video.

In travis, videos used were locally stored and accessed by the server through their file locations.26

These are the screens of the web application during the selection of videos.27

Invoke Matlab processAfter choosing a video to process, a java servlet invokes a Matlab instance to process the video. With the URL as the input, the servlet passes in the value of the video directory.

When the process button is clicked, a Matlab instance would be invoked.28

Architectural DesignVideo AcquisitionVideo InputFrame ExtractionVehicle DetectionStatistics Generation

The next sub-module is Frame extraction29

Frame ExtractionThe input video is converted into frames to prepare them for processing in the Vehicle Detection module.

In this sub-module, video frames are extracted and processed one by one.

Architectural DesignVideo AcquisitionVehicle DetectionObject DetectionTrackingClassificationCountingCongestion EstimateStatistics Generation

The next module that we would discuss is the Vehicle Detection Module. This module covers majority, if not all, of the processing in TraVis.

Vehicle Detection Module

We begin with the frames as inputs. Then the object detection sub-module prepares the frames for vehicle tracking. Once the vehicles are tracked, they are classified according to the types mentioned earlier. Vehicle counts are done when vehicles have been classified. The estimation of traffic congestion level follows when necessary data are obtained such as the vehicle counts and the classifications. Once these data are gathered, they are inserted into the database to be displayed to the user.

(We start with the flow of the vehicle detection. First, a sequence of images would undergo object detection algorithms. These potential vehicles are then tracked, classified and counted. After getting the counts, estimation of the congestion would be done and then all of the data w