Detection of Texting While Walking in Occluded Environment Using Variational Autoencoder for Safe Mobile Robot Navigation

As autonomous mobile robots begin to populate public spaces, it is becoming increasingly important for robots to accurately distinguish pedestrians and navigate safely to avoid collisions. Texting while walking is a common but hazardous behavior among pedestrians that poses significant challenges for robot navigation systems. While several studies have addressed the detection of text walkers, many have overlooked the impact of occlusions, a very common phenomenon where parts of pedestrians are obscured from sensor's view. This study proposes a machine learning method that distinguishes text walkers from other pedestrians in video data. The proposed method processes each video frame to extract body keypoints, encodes the keypoints into a latent space, and classifies pedestrian activities into three categories: normal walking, texting while walking, and other activities. A variational autoencoder is incorporated to enhance the system's robustness under various occlusion scenarios. Performance tests in real-world environments identified potential areas for improvement, particularly in distinguishing pedestrian activities with similar body postures. However, ablation studies demonstrated that the proposed system performs reliably across different occlusion scenarios.