Computer Vision Research Group

APSRA : Automatic Person identification from CCTV video feeds installed at Security Risk Areas
Person Identification from CCTV

Recently the immense cost of successful terrorist attacks on soft targets such as mass transport systems has indicated that forensic analysis of video after the event is simply not an adequate response. Indeed, in the case of suicide bombings there is simply no possibility of prosecution after the event and thus no deterrent effect. A pressing need is emerging to monitor all surveillance cameras in an attempt to detect events and persons-of-interest. One important issue is the fact that human monitoring requires a large number of people, resulting in high ongoing costs. read more

Coregistration of Space-borne Optical and SAR Satellite Images
Image Coregistration

In recent year, Synthetic Aperture Radar (SAR) Imaging has emerged as the standard tool for the Earth Observation. Spaceborne SAR satellites provide complex images of the Earth, containing information about the scattering characteristics of the viewed terrain. These images find use in numerous applications in various fields of science, such as meteorology, oceanography, agriculture, forestry, hydrology, security, military reconnaissance, cartography, navigation, etc. The information provided by SAR sensors in space can surely be complemented by the information provided by Optical Images (such as those offered by Google Earth). The basic aim of this project is to achieve data fusion among Optical and SAR imaging sensors. read more

Vehicle Make and Model Recognition
Vehicle Make and Model Recognition

Automatic vehicle type recognition (make and model) is very useful in secure access and traffic monitoring applications. It complements the number plate recognition systems by providing a higher level of security against fraudulent use of number plates in traffic crimes. We developed a simple but powerful probabilistic framework for vehicle type recognition that requires just a single representative car image in the database to recognize any incoming test image exhibiting strong appearance variations, as expected in outdoor image capture e.g. illumination, scale etc. read more

License Plate Detection and Tracking
Real Time Vehicle License Plate Detection and Tracking

The number of on-road motor vehicles has increased with the rapid growth of world's economy and with this augmentation the need for security and monitoring of vehicles has also increased. It is a necessity for officials to continuously examine the traffic to avoid congestion, over speeding and unlawful activities that involve any vehicle or to find and track a stolen vehicle. Video indexing is another modern application that can be efficiently done by vehicle's number plate recognition for road surveillance videos. read more

Automatic Vehicle Speed Esitmation from CCTV Feeds
Vehicle Speed Estimation

Currently radars /laser based systems are used to estimate velocity of vehicles on highways. However these systems require human interaction to detect the speed violations by vehicles. In this project we aim to develop a system which will accurately estimate the velocity of vehicles using CCTV cameras and detect any speed limit violations. Since CCTV cameras are already deployed on freeways for the purpose of traffic monitoring and surveillance. So the proposed system will also reduce the overall cost of velocity estimation system. read more

Text and Logo Detection in CCTV Video Feeds
Text and Logo Detection

In natural scene images, texts provide highly useful information and can provide a key for understanding the image content. Similarly, the rapid growth of video data creates a need for efficient content-based browsing and retrieving systems. Text in various forms is frequently embedded into images to provide important information about the scene like names of people, titles, as road-sign Identification, interpretation of documents such as geographic maps or engineering drawings, locations or date of an event in news video sequences and natural scene images, etc. Therefore, text should be detected for semantic understanding and image indexation. read more

Object Detection and Categorization in Natural Scenes
Object Detecion

While imitating human performance, cognitive technical systems should have an automatic visual component. In order to be useful such a component should be able to recognize many different object classes, as usually many objects are visible in an observed scene, and as goals of the system may vary strongly. This is only possible with generic object detection methods which can be trained to detect specific objects in a short time period. Presently, the most promising approach to achieve this goal is the use of unsupervised object categorization. The procedure is started with the extraction of shift-, rotation- and scale-invariant features at the location of prominent points in the image. The extracted parameters include a description of the location relative to other features. read more

WeSurv cellular Public Surveillance System

WeSurv is a surveillance system, whose core is a common user's cell phone camera. WeSurv collects surveillance data and statistics from ordinary mobile users to assist security agencies and police. The idea is to form a compact security database without using costly CCTV network infrastructure and manpower to analyze that data. A message may be broadcasted to people in sensitive area to look around with their mobile camera. The software in ordinary citizen's mobiles analyzes the video data and sends out useful text and video information to central server which is accessible to police and other concerned authorities or users. read more

Past Projects

IRIS Recognition under Noisy Conditions
IRIS Recognition

Current research in Iris Recognition focuses on efficient iris localization and more discriminative feature descriptions for noisy iris images. Our research focuses on acheiving robust iris encoding and matching on highly noisy data of the UBIRIS.v2 database. We have experimented with various new features for iris encoding and made use of different matching strategies to achieve maximum inter class dissimilarity and minimum intra-class dissimilarity. read more

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