The department of CSE has set up a few labs in order to carry out research in the identfied thrust areas. These labs have facilites to conduct research at all levels. Students from Undergraduate and Postgraduate programmes are actively involved in this lab to do research. Also research scholars and faculty members guide these students to do research in these labs. We aim at setting up a reseach hub that would eventually become a center of excellence.

Amrita Multi Dimensional Data Analytics (AmuDa) Laboratory

The Amrita Multidimensional Data Analytics Lab has been set up to enable research in large scale intelligent information systems.

With the world facing unprecedented challenges, technology has a major role to play in efficiently solving many of them. Our goal is to develop large-scale information systems that can help tackle challenges in education, healthcare and support smart buildings and cities.

Towards this we focus on end-end solutions that include designing innovative data models, scalable data management solutions, novel indexing techniques, intelligent retrieval and event-detection algorithms.

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Amrita-Cognizant Innovation (AmCoiL) Laboratory

This lab was established in the year 2012 with the support of Cognizant Technology solutions,India.

Amrita Cognizant Innovation Lab has been setup to conduct research in the areas of vision, Medical Image  processing, Machine Learning,Surveillance,Cognitive Networking,cloud computing and data analysis. The objective is to develop research areas that can provide real time solutions to the industries.

The Amrita- CTS Lab is a state-of-art facility equipped with many specialized and general purpose equipment. The specialized equipment provides researchers an opportunity to experiment and test on many innovative ideas. 

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Mobile and Wireless Networks Laboratory

Mobile and Wireless Networks Laboratory (MWN Laboratory) is functioning under the department of Computer Science and Engineering of Amrita School of Engineering, Coimbatore. MWN Lab caters to the needs of various hardware and software modules for Undergraduate, Graduate Students and Research Scholars of CSE,ECE and EEE departments to carry out their research works in the domain of Mobile and Wireless Networks.

MWN Laboratory is equipped with various Hardware modules like ARDUINO Boards, Scratch Sensor Boards, Single Board Computer based on Mini2440 ARM9 Processor, Hawk Board, Raspberry Pi Board, BlueTooth, ZigBee, Texas CC2500 Wireless Modules, Wi-Fi Modules, GSM/GPRS, GPS Development Boards, LF RFID, HF RFID Development Boards along with Android Based Aakash tablets. 4 PCs, HF-RFID Reader, ARM 9 based Single Board Computer-2, CC2500 , GSM, ZigBee, Bluetooth Modules, Raspberry Pi based SBC TI’s E2430-RF2500 KitA Test Bed integrated with Wired Ethernet, ZigBee, BlueTooth, Wi-Fi, GSM Technologies has been designed and this facility is available for the Researchers to carry out research in the domain of Smart Energy Management.

A few of the on going research projects are:

  • Third Eye – An Assistive Technology for Visually Challenged Person for easy navigation.
  • Smart Energy Management aided by Heterogeneous Wireless Technologies.
  • Provision of Secure and Reliable Wireless Connectivity between Gluco-meter and Insulin Pump.
  • Energy and Context Aware Wireless Sensor Networks.

Signal Processing and Mobile Applications (SigMa)Laboratory

SiGMA Lab is a research laboratory in the Department of Computer Science and Engineering that promotes research on Signal Processing, GPU Computing and Mobile Applications. Thrust areas include innovative signal processing applications, higher dimensional signal analysis algorithms and their adaptation to Mobile platform. Multimedia signal processing that contain structurally parallel data flows and involve iterative, computationally intensive, and time-consuming mathematical operations are the focus. 

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A framework for event modeling and detection for Smart Buildings using Vision Systems


With the government of India’s focus on “Digital India”, building Smart Cities is one of its major initiative. For smart cities to be built or renovated, it is essential that the existing buildings and infrastructure become smarter. In this project we aim to develop a software framework on which applications can be developed for buildings, to respond to emergencies and disaster management. In this context we refer building to include indoor spaces (rooms/halls/closed areas) and outdoor spaces (floors, stair cases, corridors, open spaces). Pervasive systems are necessary to make existing buildings smarter and to respond to emergencies. Ability to identify critical events, assets and people and answer to queries about their location in a building is critical to many applications; integrating object recognition with spatiotemporal analysis enhances the system to handle crisis. Major aspects that are essential for such an aspect of smart building are:  

  • Identify events in a building: Locating crowded areas, identifying events such as fire, rain, smoke, electric burst, water burst are some of the major events in a building; detecting anomalies like unexpected overcrowding is of high importance. This is of high significance for security, disaster management and to manage resources at prime locations;
  • Object Identification and tracking: Identifying objects which are extremely significant such as major assets (in terms of cost and usage value); tracking their movement is equally important to help people to locate the objects in emergencies.
  • In this project, we aim to make existing buildings smarter and intelligent by developing algorithms with data from low-cost vision systems. We shall also consider the standards available in OGC to model such events and represent the voluminous data.

In this project, we aim to make existing buildings smarter and intelligent by developing algorithms with data from low-cost vision systems. We shall also consider the standards available in OGC to model such events and represent the voluminous data.

Specific Use cases: This proposed project will be more useful in places like hospitals, large University campuses, shopping malls where a large number of people and objects move from time to time. In these places the above mentioned aspects are of high significance.

Though many researchers have developed algorithms or techniques or methods to perform event detection, object identification and tracking, most of them have developed and tested it for a known dataset (or a publicly available dataset). Very less work has been reported on such techniques for a real-time environment. Thus in Indian context, our proposed project and research will be significant, as we are planning to develop and deploy this in an existing building, thus making it become “smarter” to cater to safety of people. In precise, this work will focus on safety and security of people and objects in a building and to connect to first responders in case of emergencies.

The proposed system includes setting up cameras and vision systems in a building, developing smart applications that run over this infrastructure and the underlying database system to record events and the videos. The major components of this project would be:

Setting up vision systems: This would be required to detect events, objects, people, identify location, and monitor the infrastructure. There are a variety of cameras and vision systems available in the market such as night vision cameras, IP cameras, thermal imaging cameras, web cameras etc. Our goal is to utilize the experience we have gained from our pervious project Indoor Information Representation and Management System (IIRMS) project funded by NRDMS, DST where some work on event detection has been done [19]. Identifying suitable cameras and selecting the appropriate locations for day and night vision will be the highest priority.

Developing Applications: Once the infrastructure is set, we propose to develop efficient algorithms for object identification and tracking; unusual event detection and raising alarms/ warnings.

At the end of the project, we plan to have a complete study suggesting the best location for placing cameras/ vision systems for a smart building, a framework for configuring vision systems, a suite of algorithms to support object identification and tracking and specific events detection.

Key words: Event detection, image and video sequences, people tracking, vision systems


The broad objective of this project is to develop a) a framework for smart buildings that supports efficient indoor tracking algorithms for detecting events such as crowded areas, fire, smoke etc. using vision systems b) identification and tracking of moving objects over indoor spaces. This framework will complement the indoor information system developed as a part of the earlier IIRMS project funded by DST. We shall also explore integration of standards like SWE over IndoorGML and extending IndoorGML to include representation of objects and event is another important goal, which can help standardize the smart building applications. Specific objectives include the following, efficient algorithms will be developed, tested and deployed:

Specific Objective 1: Develop algorithms and build application for identifying unexpected events like locating crowded areas, fire detection, rain detection, smoke detection, electrical burst, and water burst in a given building.

Specific Objective 2: Develop algorithms and build application for identifying major assets and tracking their movement from one place to another from time to time (spatiotemporal) in this environment.