Flexible Human Behavior Analysis Framework for Video Surveillance Applications

Abstract EN

We study a flexible framework for semantic analysis of human motion from surveillance video.

Successful trajectory estimation and human-body modeling facilitate the semantic analysis of human activities in video sequences.

Although human motion is widely investigated, we have extended such research in three aspects.

By adding a second camera, not only more reliable behavior analysis is possible, but it also enables to map the ongoing scene events onto a 3D setting to facilitate further semantic analysis.

The second contribution is the introduction of a 3D reconstruction scheme for scene understanding.

Thirdly, we perform a fast scheme to detect different body parts and generate a fitting skeleton model, without using the explicit assumption of upright body posture.

The extension of multiple-view fusion improves the event-based semantic analysis by 15%–30%.

Our proposed framework proves its effectiveness as it achieves a near real-time performance (13–15 frames/second and 6–8 frames/second) for monocular and two-view video sequences.