Showing papers by "Kaushik Deb published in 2018"

Smoke Detection Method Based on LBP and SVM from Surveillance Camera

Abstract: Wildfire is a regular incident worldwide today. It destroys forests and also the living areas of wild animals. So, to reduce the harmful effects of such disasters this paper describes a method of smoke detection for surveillance cameras. This smoke detection will ease the fire detection. Proposed method is based on Local Binary Pattern (LBP) and Support Vector Machine (SVM). Initially, Approximate Median Filtering Algorithm was applied to subtract the background from input frame. Then, shape based filtering method was applied to get the region of interest. Thirdly, LBP values and histograms were calculated from the pixels of region of interest to form a feature vector. The proposed method also applied Bhattacharyya coefficients to verify the smoke region for accurate result. Finally, SVM classified the region of interest as smoke image. Results using real scene data show that the proposed method can give accurate results in different conditions of real world situations.

Stairways detection based on approach evaluation and vertical vanishing point

Abstract: Stair region detection and distance estimation from a stair image are challenging activities to support visually impaired navigation safely in unknown environments. In this paper, a framework is proposed for detecting stair region from a stair image utilising some natural and unique property of a stair. One unique property of them is, every stair step's beginning and ending horizontal edge point intersects with two vertical edge points creating three connected point (TCP). The TCPs are used to validate the stair edge segments and calculate the vertical vanishing point to justify the stair edges. This justification ensures that validated edge segments are arranged in an increasing parallel order which is the other unique property of a stair. These increasing edge segments are verified by utilising the y coordinate value of the vanishing point and the detection of stair candidate region is confirmed by these properties. In addition, the triangular similarity is used for distance estimation from camera to stair. The proposed framework is tested using various stair images under a variety of conditions and results are presented to demonstrate the efficiency and effectiveness.

Geometrical Feature Based Stairways Detection and Recognition Using Depth Sensor

Abstract: Stairways detection and distance measurement have been a continuous challenge of research area in human-system interaction to reach topnotch solution with greater portability in assisting visually impaired people and guiding autonomous navigation system at smart environments in the real world. For that, a framework is proposed in this work to detect the stair region from depth stair image based on a unique geometrical feature of a stair. The unique geometrical feature is every stair step's height gradually decreases from bottom to top of the stair. For that initially, the depth image is preprocessed and extracted the Canny edge image. After that, a proposed edge linking procedure is utilized through the Brute-Force Search technique to improve the broken edges. Furthermore, a non-candidate edge elimination procedure is used to extract the longest potential concurrent horizontal edge segment by considering the orientation of the horizontal edges. Finally, the extracted potential concurrent horizontal edge segment is verified as stair edge segment by justifying the aforementioned unique feature of stair and detects the stair region of interest (ROI). Furthermore, one-dimensional depth feature is extracted from the ROI and sent to the support vector machine (SVM) for recognizing the up, down, and negative stair. The distance of the recognized stair region from the camera is estimated based on the depth feature. Stairs images captured under different lighting conditions have been used to test the proposed framework to evaluate the resultant accuracy of the system.

India’s Energy Demand and Supply

Abstract: This chapter examines India’s energy demand profiles and supply options. Unlike other markets, the energy sector is slow moving and changes in consumption and production profiles are a result of lumpy investment decisions as also gradual improvements in efficiency. As a result, India’s energy sector profile appears unchanged since 1980, with coal and oil dominating the energy mix. This broad average, however, masks a significant shift away from coal and towards oil until 2000s, and the subsequent recovery in coal’s share. The competition between coal and oil in the last century has now been played out between coal and gas over a much shorter period. The outcome has been an energy mix that is dominated by coal with some gains being made by renewables. In addition, India remains significantly import dependent for all forms of fossil fuels.