International Journal of Advanced Networking and Applications

T. Venkatesh ¹, U. N. Sinha ²

Affiliations
1 Dept. of Computer Science and Engineering, G.C.E, Ramanagaram, IN
2 CSIR-NAL/CMMACS, Bangalore- 560017, IN

Abstract

Parallel processing is the only alternative for meeting computational demand of scientific and technological advancement. Yet first few parallelized versions of a large application code- in the present case-a meteorological Global Circulation Model- are not usually optimal or efficient. Large size and complexity of the code cause making changes for efficient parallelization and further validation difficult. The paper presents some novel techniques to enable change of parallelization strategy keeping the correctness of the code under control throughout the modification.

Keywords

overlap between Computation and Communication, Parallelization Strategy, Scalable Parallel Computing, Software Engineering.

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N. A. Deepak ¹, U. N. Sinha ¹

Affiliations
1 Flosolver Division, National Aerospace Laboratories (NAL), Bangalore, IN

Abstract

Human detection and recognizing their actions from the captured video streams is more complex and challenging task in the field of image processing. The human action recognition is more complex due to variability in shapes and articulation of human body, motions in the background scene, lighting conditions and occlusion. Human actions are recognized by tracking the selected object over the consecutive frames of gray scale image sequences, initially the background motion of the input video stream is subtracted, and its binary images are constructed, the object which needs to be monitored is selected by enclosing the required pixels within bounding rectangle, by using spatio-temporal interest points (Mo-SIFT). The selected foreground pixels within the bounding rectangle are then tracked using edge tracking algorithm over the consecutive frames of gray scale images. The features like horizontal stride (HS) and vertical distance (VD) are extracted while tracking and the values of these features from the current frame are subtracted with the previous frame values to know the motion. The obtained results after subtraction are then compared with the selected threshold value to predict the type of human action using linear prediction technique. This methodology finds an application where monitoring the human actions is required such as shop surveillance, city surveillance, airports surveillance and other places where security is the prime factor.

Keywords

Background Subtraction, Edge Tracking, Linear Prediction, Occlusion, Spatio-Temporal Interest Points (Mo-SIFT), Surveillance, Threshold.

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