Image segmentation based on histogram of depth and an application in driver distraction detection

TL;DR

This paper introduces a depth image segmentation method using histogram analysis and adaptive region growing, applied to real-time driver distraction detection by tracking driver movements and identifying distracted behaviors.

Contribution

The study presents an adaptive threshold-based region growing approach for depth image segmentation, specifically tailored for driver distraction detection applications.

Findings

Effective segmentation of human objects from depth images.

Successful real-time detection of distracted driving activities.

Demonstrated robustness in various distracted behaviors.

Abstract

This study proposes an approach to segment human object from a depth image based on histogram of depth values. The region of interest is first extracted based on a predefined threshold for histogram regions. A region growing process is then employed to separate multiple human bodies with the same depth interval. Our contribution is the identification of an adaptive growth threshold based on the detected histogram region. To demonstrate the effectiveness of the proposed method, an application in driver distraction detection was introduced. After successfully extracting the driver's position inside the car, we came up with a simple solution to track the driver motion. With the analysis of the difference between initial and current frame, a change of cluster position or depth value in the interested region, which cross the preset threshold, is considered as a distracted activity. The experiment results demonstrated the success of the algorithm in detecting typical distracted driving activities such as using phone for calling or texting, adjusting internal devices and drinking in real time.