Programs
- M. Tech. in Automotive Engineering -
- Fellowship In Interventional Pulmonology and Pulmonary Critical care - Fellowship
With focus on digital buildings, digital campus and digital city, this lab has been setup in 2017 by the Department of Computer Science and Engineering with funding from Department of Science and Technology, Government of India under project id: F.NO NRDMS/01/175/016 G & C.
The objective is to develop software applications for smart buildings using vision systems. The lab is equipped with specialized high-end computing servers, GPU workstations and computing devices suitable for running image and video applications. With cameras fitted in various locations of the building, real time data in the form of video and images are acquired and stored in a server for further analysis. Thermal image acquisition systems are available to develop a variety of applications. A project titled “A framework for event modeling and detection for Smart Buildings using Vision Systems “ has been delivered to DST from this laboratory. 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. This software framework developed for buildings can respond to emergencies and disaster management. The developed software framework can be used in places like hospitals, large University campuses, shopping malls where a large number of people and objects move from time to time. In precise, this lab focuses on safety and security of people and objects in a building and to connect to first responders in case of emergencies.
The application has been developed as a open source facility that can capture live video and analyze for events like crowd detection, rainfall detection, smoke detection, fire detection and object identification. The software framework has the following facilities:
Research Team
Latha Parameswaran, Gautam, K. S.,and Senthil Kumar Thangavel.
Unraveling meaningful pattern form the video offers a solution to many real-world problems, especially surveillance and security. Detecting and tracking an object under the area of video surveillance, not only automates the security but also leverages smart nature of the buildings. The objective of the manuscript is to detect and track assets inside the building using vision system. In this manuscript, the strategies involved in asset detection and tracking are discussed with their pros and cons. In addition to it, a novel approach has been proposed that detects and tracks the object of interest across all the frames using correlation coefficient. The proposed approach is said to be significant since the user has an option to select the object of interest from any two frames in the video and correlation coefficient is calculated for the region of interest. Based on the arrived correlation coefficient the object of interest is tracked across the rest of the frames. Experimentation is carried out using the 10 videos acquired from IP camera inside the building.
Research Team
Latha Parameswaran, Gautam, K. S., Senthil Kumar Thangavel.
The search for the digital image from the repository is challenging since the volume of the image created and consumed is growing exponentially with respect to time. This makes the image retrieval an ongoing research problem. Rather than relying on metadata, analyzing the content of the image is proven to be a successful solution for retrieval. Since manually annotating the growing images is considered to be impossible. Thus the need for the hour is a framework that is capable of retrieving the similar images with less time complexity. In this paper, an image retrieval framework has been proposed to retrieve similar images from the repository. The motivation of the work is to leverage the smart nature of building, in such a way that when an asset inside the building is captured and given as query image, the user of the building should be provided with all the relevant assets inside the building. The proposed framework is built with color, shape and edge retrieval system that works in cascade approach. Since the framework works as a cascade, it is observed that the results get fine-tuned at every layer of the cascade thus increasing the precision. The novelty of the work lies in the third layer where the XOR operation is performed to check the magnitude of dissimilarity between the query and database images. Based on the above dissimilarity, the threshold has been fixed to differentiate the image of interest from other images in the repository. The performance of the proposed approach is evaluated with the manually built dataset. On evaluating the performance, it is inferred that the precision of the proposed framework is 99%.
Research Team
Kumar, Latha Parameswaran, D. Kavin, and Senthil Kumar Thangavel
Object recognition is one of the essential Computer Vision techniques. The success of object recognition lies in identifying features that strongly represent the object of interest. The manuscript comes up with a hybrid feature descriptor that combines the properties of HOG, ORB and BRISK feature descriptors. Linear SVM is used to classify the feature vectors of the object of interest and other objects in the scene. Occlusion, Orientation and Scaling are some of the limitations in existing approach. From the experimental analysis, we infer that the proposed framework handles partial occlusion and is invariant to scaling and rotation. The framework has been tested with a manually built data library and the classification accuracy of the proposed framework is 0.91, whereas the standalone performance of the HOG, ORB and BRISK are 0.85, 0.87, and 0.89 respectively.
Research Team
Latha Parameswaran,Sridhar, P., and Senthil Kumar Thangavel.
The projected work shows generic rule in YCbCr color space based fire pixel detection is proposed for smart building which will complement the conventional electronic sensor based fire detection system. The proposed method handles YCbCr color model is used for decoupling the luminance and chrominance which added discriminate the color than RGB color model. This algorithm has been tested on fire and fire like images which results in 97.95% detection accuracy. Obtained experimental results have been compared with other existing algorithms and it is observed that gives a very high the proposed algorithm detection accuracy and feasible true positive rate in fire images.
Research Team
Sridhar, P, Manickam, M. T., Yogesh, M., Thangavel, S. K., & Parameswaran, L.
Unraveling meaningful pattern form the video offers a solution to many real-world problems, especially surveillance and security. Detecting and tracking an object under the area of video surveillance, not only automates the security but also leverages smart nature of the buildings. The objective of the manuscript is to detect and track assets inside the building using vision system. In this manuscript, the strategies involved in asset detection and tracking are discussed with their pros and cons. In addition to it, a novel approach has been proposed that detects and tracks the object of interest across all the frames using correlation coefficient. The proposed approach is said to be significant since the user has an option to select the object of interest from any two frames in the video and correlation coefficient is calculated for the region of interest. Based on the arrived correlation coefficient the object of interest is tracked across the rest of the frames. Experimentation is carried out using the 10 videos acquired from IP camera inside the building.
Research Team
Sridhar, P, Srishilesh, P. S., Parameswaran, L., Tharagesh, R. S., Thangavel, S. K.
Fire outbreak has become a common accident that occurs in several places such as in forests, manufacturing industries, living house and in widely crowded areas. These incidents cause severe damage to nature as well as to living creatures in the affected surroundings. Due to this, the need for efficient fire detection system has been increased rapidly. Using fire detecting sensors has proved to be an efficient solution but its effectiveness on delivering quick results depends on the affinity of fire sources. In the proposed method, we present an economical and affordable fire detection algorithm using video processing techniques which is compatible with CCTV and other stationary surveillance cameras. The algorithm uses an RGB color model with chromatic and dynamic disorder analysis to detect the fire. Fire pixels are detected by the rules of the color model which is mainly dependent on the fire pixel intensity and also the saturation of red color component in the fire pixel. The extracted fire like pixels are authorized by growth combined with the disorder of the fire regions. Furthermore, based on iterative checking the real fire is identified, if it is present then the appropriate signals will be sent. The proposed method is tested on various datasets acquired in real time environments and from the internet. This methodology can be used for fully automatic fire detection surveillance with reduced false true errors.
Research Team
Latha Parameswaran, R. Manjusha
Crowd analysis has found its significance in varied applications from security purposes to commercial use. This proposed algorithm aims at contour extraction from skeleton of the foreground image for identifying and counting people and for providing crowd alert in the given scene. The proposed algorithm is also compared with other conventional algorithms like HoG with SVM classifier, Haar cascade and Morphological Operator. Experimental results show that the proposed method aids better crowd analysis than the other three algorithms on varied datasets with varied illumination and varied concentration of people.
Research Team
R. Manjusha,Swati Nair R. Manjusha, Latha Parameswara
The Exploration of contextual information is very important for any automatic image annotation system. In this work a method based on kernels and keyword propagation technique is proposed. Automatic annotation with a set of keywords for each image is carried out by learning the image semantics. The similarity between the images is calculated by Hellinger’s kernel and Radial Bias Function kernel(RBF)kernel. The images are labelled with multiple keywords using contextual keyword propagation. The results of using the two kernels on the set of features extracted are analysed. The annotation results obtained were validated based on confusion matrix and were found to have a good accuracy. The main advantage of this method is that it can propagate multiple keywords and no definite structure for the annotation keywords has to be considered
Research Team
R. Manjusha, Y. Himabindu, Latha Parameswaran
Dynamic climatic conditions like rain, affects the performance of vision algorithms which are used for surveillance and analysis tasks. Removal of rain-drops is challenging for single image as the rain drops affect the entire image and makes it difficult to identify the background affected by the rain. Thus, removal of rain from still pictures is a complex and challenging task. The rain drops affect the visibility and clarity of the image which makes it difficult to read and analyze the information present in the image. In this paper, we identified and restored the rain drop affected regions using a deep learning architecture.
Research Team
R. Manjusha, Padmashini, M., Dr. Latha Parameswaran
Estimating the number of people in a particular scene has always been an important topic of research in computer vision and digital image processing. People counting has wide applications in scenario ranging from analyzing the customer’s choice and improving the quality of service in retail stores, supermarkets and shopping malls to managing human resources and optimizing the energy usage in office buildings. While there exists algorithms for counting people in a scene, some algorithm have set their benchmark in performance with respect to efficiency, flexibility and accuracy. In this paper, an attempt has been made to perform people counting using Deep Neural Networks (DNN) on comparison with existing image processing based algorithms like Histogram of Oriented Gradients with Support Vector Machine (HoG with SVM), Local Binary Pattern (LBP) based Adaboost classifier and contour based people detection. The proposed DNN based approach has higher accuracy at 90% and less false negatives.
Sr. No. | Title of the Project | Funding Agency | Amount Sanctioned in Lakhs (Rs.) | Status |
1 | A Framework for event modelling Detection for Smart Building using vision System | DST | 40.64 | Completed |
Principal Investigator, Professor, Department of CSE
Principal Investigator, Professor, Department of CSE
Generic Rule in YCbCr color space based Fire Pixel Detection, JRF, Computer Science and Engineering
Smart Buildings using Computer Vision, JRF, Department of Computer Science and Engineering
Smoke Segmentation, Water Burst Detection, JRF, Department of Computer Science and Engineering
Object Detection & Human Tracking, JRF, Department of Computer Science and Engineering
Image Processing, Assistant Professor, Computer Science and Engineering
Event detection, image annotation and scene description, JRF, Department of Computer Science and Engineering
Object Detectors in Traffic Lights, Department of Computer Science and Engineering
Wireless networks, Discrete Tomography, Cognitive Radio Networks, Department of Computer Science and Engineering
Lead Developer of Smart Spaces Lab
Bachelor of Technology in Computer Science and Engineering
Smoke Detection
Bachelor of Technology in Computer Science and Engineering
Visualization and Machine Learning
Bachelor of Technology in Computer Science and Engineering
Optimization of Key Frame Extraction
Bachelor of Technology in Computer Science and Engineering
Object detection and Recognition
Bachelor of Technology in Computer Science and Engineering
Implementation of Generic fire color model
Bachelor of Technology in Computer Science and Engineering
Object Detection Using Neural Network
Bachelor of Technology in Computer Science and Engineering
Machine Learning
Bachelor of Technology in Computer Science and Engineering
Android Application – Machine Learning
Bachelor of Technology in Computer Science and Engineering
Deep Learning Algorithms
Bachelor of Technology in Computer Science and Engineering
Dynamic camera assignment and handoff
Bachelor of Technology in Computer Science and Engineering
Thermal Image Processing
Master of Technology in Computer Science and Engineering
Deep Learning and Data Analytics
Bachelor of Technology in Computer Science and Engineering
Data Processing
Bachelor of Technology in Computer Science and Engineering
Embedded Systems
Bachelor of Technology in Computer Science and Engineering
Fire Detection using Image Processing
Bachelor of Technology in Computer Science and Engineering
Object Tracking, Smoke and Fire Detection
Bachelor of Technology in Computer Science and Engineering
Framework for Event modelling
Bachelor of Technology in Computer Science and Engineering
Object Detection using Neural Network
Bachelor of Technology in Computer Science and Engineering
Video Based Fire Detection
Bachelor of Technology in Computer Science and Engineering
Embedded Systems using Rasberry Pi
Bachelor of Technology in Computer Science and Engineering
Sr. No. | Component List | No. of Items |
1 | CP PLUS 4MP OUT DOOR IPCAMERA 36MM | 4 |
2 | CP PLUS SPEED DOME 30XOPTICAL IP CAMERA | 1 |
3 | UTEP 8 PORT POE SWITCH 30 V WITH CCTV MODE | 1 |
4 | DAHUA 4K 2 SATA 16CHNVR | 1 |
5 | JOYSTICK IP SPEED DOME CAMERA CP PLUS | 1 |
6 | FLUKE THERMAL IMAGER TiS 40 | 1 |
7 | HP DL380 Gen9 2U Rack Server | 1 |
8 | Lenova Work Station | 4 |
9 | SATA 4 Tb | 1 |
10 | SEAGATE 1 Tb ,External HDD | 1 |
11 | Rasberry Pi Camera moduleV-2-8 Mega Pixel 1080p | 1 |
12 | SEAGATE 1 Tb Backplus Slim | 1 |
13 | Logitech c270 Webcam | 3 |