Visual Tracking of Human Head and Arms Using Adaptive Multiple Importance Sampling on a Single Camera in Cluttered Environments

This paper presents a 2D upper body tracking algorithm using a single monocular camera. The proposed method can be applied on a stationary or moving camera platform, and is able to achieve real-time performance in cluttered environments, making it ideal for human-machine interaction. The algorithm extracts body parts even when the target person approaches other objects. This is a common problem for depth-based camera systems. Real-time visual extraction of a subject's head and arms is performed during preprocessing in order to determine their current action and presents two key innovations. First, multiple visual clues are integrated dynamically by an adaptive multiple importance sampling particle filter to generate hypotheses. These hypotheses can efficiently estimate various gestures of arms on images captured from cluttered environments. Second, multiple visual cues of a human face and arms are devised, which quickly and effectively verifies various hypotheses from the multiple importance sampling schemes. To validate the effectiveness of the proposed tracking approach, several experiments are performed whose results appear to be quite promising.