elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Multiple vehicle and people tracking in aerial imagery using stack of micro single-object-tracking CNNs

Bahmanyar, Reza and Azimi, Seyedmajid and Reinartz, Peter (2019) Multiple vehicle and people tracking in aerial imagery using stack of micro single-object-tracking CNNs. ISPRS. ISPRS International GeoSpatial Conference, 12.-14. Oct. 2019, Tehran, Iran.

[img] PDF
11MB

Official URL: https://geospatialconf2019.ut.ac.ir/

Abstract

Geo-referenced real-time vehicle and person tracking in aerial imagery has a variety of applications such as traffic and large-scale event monitoring, disaster management, and also for input into predictive traffic and crowd models. However, object tracking in aerial imagery is still an unsolved challenging problem due to the tiny size of the objects as well as different scales and the limited temporal resolution of geo-referenced datasets. In this work, we propose a new approach based on Convolutional Neural Networks (CNNs) to track multiple vehicles and people in aerial image sequences. As the large number of objects in aerial images can exponentially increase the processing demands in multiple object tracking scenarios, the proposed approach utilizes the stack of micro CNNs, where each micro CNN is responsible for a single-object tracking task. We call our approach Stack of Micro-Single-Object-Tracking CNNs (SMSOT-CNN). More precisely, using a two-stream CNN, we extract a set of features from two consecutive frames for each object, with the given location of the object in the previous frame. Then, we assign each MSOT-CNN the extracted features of each object to predict the object location in the current frame. We train and validate the proposed approach on the vehicle and person sets of the KIT AIS dataset of object tracking in aerial image sequences. Results indicate the accurate and time-efficient tracking of multiple vehicles and people by the proposed approach.

Item URL in elib:https://elib.dlr.de/128900/
Document Type:Conference or Workshop Item (Speech)
Title:Multiple vehicle and people tracking in aerial imagery using stack of micro single-object-tracking CNNs
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Bahmanyar, Rezareza.bahmanyar (at) dlr.dehttps://orcid.org/0000-0002-6999-714X
Azimi, SeyedmajidSeyedmajid.Azimi (at) dlr.dehttps://orcid.org/0000-0002-6084-2272
Reinartz, Peterpeter.reinartz (at) dlr.dehttps://orcid.org/0000-0002-8122-1475
Date:2019
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:pp. 1-8
Publisher:ISPRS
Status:Published
Keywords:Aerial Imagery, Vehicle Tracking, Person Tracking, CNNs, Disaster Management, Traffic Management
Event Title:ISPRS International GeoSpatial Conference
Event Location:Tehran, Iran
Event Type:international Conference
Event Dates:12.-14. Oct. 2019
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Road Transport
DLR - Research area:Transport
DLR - Program:V ST Straßenverkehr
DLR - Research theme (Project):V - NGC KoFiF, V - D.MoVe, V - UrMo Digital
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Bahmanyar, Gholamreza
Deposited On:29 Aug 2019 11:08
Last Modified:07 Nov 2019 12:09

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.