Video Analytics Lab

We aspire to develop intelligent algorithms that perform important visual perception tasks.

Virtual Reality Center

we provide technological support in development of immersive technology-based simulations and related solutions.

Research Areas

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Computer Vision It deals with how computers can obtain high-level awareness from digital images or videos

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Deep LearningBranch of Artificial Intelligence (AI) function that emulate the workings of the human brain in processing data and creating patterns for use in decision-making

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Natural Language Processing Branch of Artificial Intelligence (AI) that helps computers understand, interpret and manipulate human language

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Immersive TechnologiesIt is combination of VR, AR, MR that extends creates a new reality. Provides great oppotunities in the development of simulators of the major systems

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Internet of Things It deals with network of physical objects (things) which are embedded with sensors, software, and other technologies for the purpose of connecting and transfering data

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Cloud ComputingIt deals with computing services including servers, storage, databases, networking, software, analytics, and intelligence over the Internet

Projects

Publications

Adaptive stochastic segmentation via
											energy-convergence for brain tumor in MR images

Adaptive stochastic segmentation via energy-convergence for brain tumor in MR images

Adaptive stochastic segmentation methodology proposed for tumor detection in MR images. Developed frame by using level set function globally and three energies locally. Improves each point of curve by optimizing local energies and force functions. Solved stability and convergence issues related to curve evolution process. Reduced iterations/computation time through internal and external energies/forces.

Using Relaxation to Fuse RFID and Vision for
											Object Tracking Outdoors

Using Relaxation to Fuse RFID and Vision for Object Tracking Outdoors

Fusion of Radio Frequency Identification (RFID) with Computer Vision (CV) can significantly improve performance in applications of autonomous vision and navigation, activity analysis, site monitoring, and especially in outdoor environments. RFID and CV provide both overlapping and unique information for deciding on object identity, location, and motion.

Target tracking and surveillance by fusing
											Stereo and RFID information

Target tracking and surveillance by fusing Stereo and RFID information

Ensuring security in high risk areas such as an airport is an important but complex problem. Effectively tracking personnel, containers, and machines is a crucial task. Moreover, security and safety require understanding the interaction of persons and objects. Computer vision (CV) has been a classic tool; however, variable lighting, imaging, and random occlusions present difficulties for real-time surveillance, resulting in erroneous object detection and trajectories. Determining object ID via CV at any instance of time in a crowded area is computationally prohibitive, yet the trajectories of personnel and objects should be known in real time. Radio Frequency Identification (RFID) can be used to reliably identify target objects and can even locate targets at coarse spatial resolution, while CV provides fuzzy features for target ID at finer resolution.

Detection & Classification of Vehicles in Varying
											Complexity of
											Urban Traffic Scenes

Detection & Classification of Vehicles in Varying Complexity of Urban Traffic Scenes

Detection and classification of vehicles is a paramount task in surveillance framework and for traffic management and control. The type of transportation infrastructure, road conditions, traffic trends and illumination conditions are some of the key factors that affect these essential tasks. This paper explores performance of existing techniques regarding detection and classification in local, day time, complex urban traffic videos with increased free flowing vehicle volume. Three different traffic datasets with varying level of complexity are used for analysis. The scene complexity is governed by factors such as vehicle speed, type and size of dynamic objects, direction of motion of vehicles, number of lanes, occlusion, length and camera viewing angle.

Automatic Lesion Detection System (ALDS) for Skin
											Cancer Classification Using
											SVM and Neural Classifiers

Automatic Lesion Detection System (ALDS) for Skin Cancer Classification Using SVM and Neural Classifiers

Technology aided platforms provide reliable tools in almost every field these days. These tools being supported by computational power are significant for applications that need sensitive and precise data analysis. One such important application in the medical field is Automatic Lesion Detection System (ALDS) for skin cancer classification. Computer aided diagnosis helps physicians and dermatologists to obtain a "second opinion" for proper analysis and treatment of skin cancer.

Comparative Analysis of Vehicle Detection in Urban
											Traffic Environment using Haar Cascaded Classifiers
											and Blob Statistics

Comparative Analysis of Vehicle Detection in Urban Traffic Environment using Haar Cascaded Classifiers and Blob Statistics

The applications of computer vision are widely used in traffic monitoring and surveillance. In traffic monitoring, detection of vehicles plays a significant role. Different attributes such as shape, color, size, pose, illumination, shadows, occlusion, background clutter, camera viewing angle, speed of vehicles and environmental conditions pose immense and varying challenges in the detection phase.

INSTANT ANSWERS, 5W’S & H TOOL Architecture

INSTANT ANSWERS, 5W’S & H TOOL

Instant answers search engine provides solution to the problems of visiting multiple sources, compilation effort, time consumption and incompleteness of information while using Crawler-based search engines, encyclopedias, video portals, image portal etc. for the purpose of making assignments, reports and in other research works. Instant answers search engine works with both structured and unstructured data and uses Five W’s & H to provide completeness. It involves multiple levels of classifications, selection of the correct data sources, strategies for the extraction of data with the use of multi layered Pipeline Architecture. Its ultimate goal is to provide fast, accurate, complete and compiled information for the students, researchers and knowledge seekers.

Performance Evaluation of Advanced Deep Learning Architectures for Offline Handwritten Character Recognition

Performance Evaluation of Advanced Deep Learning Architectures for Offline Handwritten Character Recognition

This paper presents a hand-written character recognition comparison and performance evaluation for robust and precise classification of different hand-written characters. The system utilizes advanced multilayer deep neural network by collecting features from raw pixel values. The hidden layers stack deep hierarchies of non-linear features since learning complex features from conventional neural networks is very challenging. Two state of the art deep learning architectures were used which includes Caffe AlexNet [5] and GoogleNet models [6] in NVIDIA DIGITS [10]. The frameworks were trained and tested on two different datasets for incorporating diversity and complexity.

Performance Evaluation of Advanced Deep Learning Architectures for Offline Handwritten Character Recognition

Understanding 5G Wireless Cellular Network: Challenges, Emerging Research Directions and Enabling Technologies

The increasing usage of smart devices and the penetration of mobile phones in the low-end markets have outpaced the average growth of this wireless mobile communications industry due to which the world is witnessing the demands of burgeoning data traffic, proliferating bandwidth and energy efficient wireless communication technologies.

Performance Evaluation of Advanced Deep Learning Architectures for Offline Handwritten Character Recognition

Pose and Expression Invariant Alignment based Multi-View 3D Face Recognition

In this study, a fully automatic pose and expression invariant 3D face alignment algorithm is proposed to handle frontal and profile face images which is based on a two pass course to fine alignment strategy. The first pass of the algorithm coarsely aligns the face images to an intrinsic coordinate system (ICS) through a single 3D rotation and the second pass aligns them at fine level using a minimum nose tip-scanner distance (MNSD) approach.

Performance Evaluation of Advanced Deep Learning Architectures for Offline Handwritten Character Recognition

Facial Asymmetry-Based Age Group Estimation: Role in Recognizing Age-Separated Face Images

Face recognition aims to establish the identity of a person based on facial characteristics. On the other hand, age group estimation is the automatic calculation of an individual’s age range based on facial features. Recognizing age-separated face images is still a challenging research problem due to complex aging processes involving different types of facial tissues, skin, fat, muscles, and bones. Certain holistic and local facial features are used to recognize age-separated face images. However, most of the existing methods recognize face images without incorporating the knowledge learned from age group estimation.

Performance Evaluation of Advanced Deep Learning Architectures for Offline Handwritten Character Recognition

Spatial Video Forgery Detection and Localization using Texture Analysis of Consecutive Frames

Now-a-days, videos can be easily recorded and forged with user-friendly editing tools. These videos can be shared on social networks to make false propaganda. During the process of spatial forgery, the texture and micro-patterns of the frames become inconsistent, which can be observed in the difference of two consecutive frames. Based on this observation, a method has been proposed for detection of forged video segments and localization of forged frames.

Performance Evaluation of Advanced Deep Learning Architectures for Offline Handwritten Character Recognition

Classification of Authentic and Tampered Video Using Motion Residual and Parasitic Layers

These days, videos can be easily recorded, altered and shared on social and electronic media for deception and false propaganda. However, due to sophisticated nature of the content alteration tools, alterations remain inconspicuous to the naked eye and it is a challenging task to differentiate between authentic and tampered videos. During the process of video tampering the traces of objects, which are removed or modified, remain in the frames of a video. Based on this observation, in this study, a new method is introduced for discriminating authentic and tampered video clips.