Human Shape Variation - An Efficient Implementation using Skeleton
This addresses the need for efficient human detection in security applications, but appears incremental as it builds on existing skeleton-based methods.
The paper tackles the problem of automatically detecting human presence in secure environments by developing a shape recognition algorithm that is robust, fast, and has low error rates, using skeletons and simple features like posture and length to process camera images quickly and generate alerts for moving objects.
It is at times important to detect human presence automatically in secure environments. This needs a shape recognition algorithm that is robust, fast and has low error rates. The algorithm needs to process camera images quickly to detect any human in the range of vision, and generate alerts, especially if the object under scrutiny is moving in certain directions. We present here a simple, efficient and fast algorithm using skeletons of the images, and simple features like posture and length of the object.