Dark Environment Motion Analysis Using Scalable Model and Vector Angle Technique

Abstract:

Detecting human existence in video streams is a fundamental task in many video processing applications. In this paper, a novel procedure is produced to model, analyze and recognize human motions (jogging and walking in dark environment) in video streams. There are four major areas that are related in this project for human motion analysis: (1) developing human body structure based on human skeleton model, (2) tracking and data collecting human motion with side view, (3) recognizing human activities from image sequences, and (4) image processing technique using edge detection and vectors angle calculation. All algorithms are developed using MATLAB software. Segmentation is developed to reduce the amount of data and filters out the useless information. Two methods are proposed for angle calculation and activities classification. Results showed that angle between 153.76°-180° for method 1 and 49.64°-92.86° for method 2 is classified as walking while jogging is 95.17°-138.72° for method 1 and 22.62°-56.31° for method 2.

• Periodical: Applied Mechanics and Materials (Volume 654)
• Pages: 310-314
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.654.310
• Online since: October 2014
• Authors: Ching Yee Yong, Rubita Sudirman, Kim Mey Chew
• Keywords: Edge Detection, Motion, Recognition, Scalable Model, Skeleton

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