Reena Behera - Profile on Academia.edu (original) (raw)

Papers by Reena Behera

Face detection using data mining approach

2015 International Conference on Communications and Signal Processing (ICCSP), 2015

Face detection has become a fundamental task in computer vision and pattern recognition applicati... more Face detection has become a fundamental task in computer vision and pattern recognition applications. This paper describes a system for face detection using data mining approach. The proposed face detection method is a two phase process comprising of training and detection phase. In the training phase, training image is transformed into an edge and non-edge image. Maximal Frequent Itemset Algorithm (MAFIA) is used to mine positive and negative feature patterns from edge and non-edge images respectively. Based on the feature patterns mined, a face detector is constructed to prune non-face candidates. In the detection phase, sliding window approach is applied to the test image in different scales. Experimental results on FEI face database show good performance even across different orientations, pose and expression variations to a certain extent.

A Novel Method for Day Time Pedestrian Detection

SAE International Journal of Passenger Cars - Electronic and Electrical Systems, 2015

Redundant Data Removal from Images

SAE Technical Paper Series, 2015

2012 World Congress on Information and Communication Technologies, 2012

In recent years, surveillance systems have gained increased importance in order to increase the s... more In recent years, surveillance systems have gained increased importance in order to increase the safety and security of people. These systems have applications in various domains like home or bank security, traffic monitoring, defense; and in public places like railway stations, malls, airports, etc. Our goal is to develop an intelligent real-time surveillance system that can help in increasing the efficiency of the system. In order to cover a large area, we need to install more number of cameras that leads to more number of videos that are to be monitored simultaneously. This in turn increases human intervention and makes it error prone. Therefore, it is of utmost importance to automate the complete system. In the proposed system, cameras are placed in such a way that there is a significant overlap between the field of view of the cameras. This helps in establishing an association between the cameras. The proposed real time surveillance system detects and tracks the objects in motion and provides automatic warning in case of suspicious activities such as unidentified object and restricted zone monitoring. The system provides different views in different cameras, thereby helping in resolving the occlusion in a simple and novel way. The algorithm has been tested on a CPU platform (Intel dual core with 4 GB RAM) with four wireless IP cameras (1.3 MP EDiMAX camera with night vision support) with a processing rate of 10 fps. This system can handle up to eight cameras on a GPU platform (NVIDIA GeForce GTX 480) with frame rate of 25fps.

Procedia Computer Science, 2015

In video analytics based systems, an efficient method for segmenting foreground objects from vide... more In video analytics based systems, an efficient method for segmenting foreground objects from video frames is the need of the hour. Currently, foreground segmentation is performed by modelling background of the scene with statistical estimates and comparing them with the current scene. Such methods are not applicable for modelling the background of a moving scene, since the background changes between the scenes. The scope of this paper includes solving the problem of background modelling for applications involving moving camera. The proposed method is a non-panoramic background modelling technique that models each pixel with a single Spatio-Temporal Gaussian. Experimentation on various videos promises that the proposed method can detect foreground objects from the frames of moving camera with negligible false alarms.