BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Department of Electrical and Computer Engineering - ECPv6.17.0//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Department of Electrical and Computer Engineering
X-ORIGINAL-URL:https://engineering.jhu.edu/ece
X-WR-CALDESC:Events for Department of Electrical and Computer Engineering
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20200308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20201101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20210314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20211107T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210318T140000
DTEND;TZID=America/New_York:20210318T160000
DTSTAMP:20210628T204301Z
CREATED:20210628T204301Z
LAST-MODIFIED:20210628T204301Z
UID:554290-1616076000-1616083200@engineering.jhu.edu
SUMMARY:Dissertation Defense: Vishwanath Sindagi
DESCRIPTION:Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.Title: Single Image Based Crowd Counting Using Deep LearningAbstract: Estimating count and density maps from crowd images has a wide range of applications such as video surveillance\, traffic monitoring\, public safety and urban planning. In addition\, techniques developed for crowd counting can be applied to related tasks in other fields of study such as cell microscopy\, vehicle counting and environmental survey. The task of crowd counting and density map estimation from a single image is a difficult problem since it suffers from multiple issues like occlusions\, perspective changes\, background clutter\, non-uniform density\, intra-scene and inter-scene variations in scale and perspective. These issues are further exacerbated in highly congested scenes. In order to overcome these challenges\, we propose a variety of different deep learning architectures that specifically incorporate various aspects such as global/local context information\, attention mechanisms\, specialized iterative and multi-level multi-pathway fusion schemes for combining information from multiple layers in a deep network. Through extensive experimentations and evaluations on several crowd counting datasets\, we demonstrate that the proposed networks achieve significant improvements over existing approaches.We also recognize the need for large amounts of data for training the deep networks and their inability to generalize to new scenes and distributions.  To overcome this challenge\, we propose novel semi-supervised and weakly-supervised crowd counting techniques that effectively leverage large amounts of unlabeled/weakly-labeled data. In addition to developing techniques with ability to learn from limited labeled data\, we also introduce a new large-scale crowd counting dataset which can be used to train considerably larger networks. The proposed data consists of 4\,372 high resolution images with 1.51 million annotations. We made explicit efforts to ensure that the images are collected under a variety of diverse scenarios and environmental conditions. The dataset provides a richer set of annotations like dots\, approximate bounding boxes\, blur levels\, etc.Committee Members \nVishal Patel\, Department of Electrical and Computer EngineeringRama Chellappa\, Department of Electrical and Computer EngineeringAlan Yuille\, Department of Computer Science
URL:https://engineering.jhu.edu/ece/event/dissertation-defense-vishwanath-sindagi/
END:VEVENT
END:VCALENDAR