Harris-SIFT Descriptor for Video Event Detection Based on a Machine Learning Approach

Video data is becoming increasingly important in many commercial and scientific areas with the advent of applications such as digital broadcasting, video-conferencing and multimedia processing tools, and with the development of the hardware and communications infrastructure necessary to support visual applications. The objective of this work is to propose a method for event detection in a video stream. We combine Harris-SIFT descriptor with motion information in order to detect human actions in video. We tested our method in KTH database and compared it to space-time interest points (STIP) descriptor. The results obtained achieved similar results to the STIP method.

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