Improved color and intensity patch segmentation for human full-body and body-parts detection and tracking
Haiwen Chen, Mike McGurr · 2014
This paper presents a new way for detection and tracking of human full-body and body-parts (head, torso, arms, and legs) with color and intensity patch segmentation. The original R, G, and B are transformed to H (hue), S (saturation), and V (value) domain, as well as to Y, I, and Q for the NTSC system. With the help of morphological image processing, the fusion of S, V, Y, I and Q segmentations are used for full-body detection, while the individual V, I and Q segmentations are used for body-parts detection. An adaptive thresholding scheme has been developed for dealing with body size changes, illumination condition changes, and cross camera parameter changes. Preliminary tests with the PETS 2014 datasets show that we can obtain high probability of detection (Pd=100%) and low probability of false alarm (Pfa=1.95%) for both full-body and body-parts. The reliable body-parts (e.g. head) detection allows us to continuously track the individual person even though the torsos and legs of several closely spaced persons are merged together, and accurate human head localization is critical for human ID (face recognition). Furthermore, the detected body-parts allow us to extract important local constellation features of the body-parts' positions and angles related to the centroid position of the full-body. These features are critical for human walk gating estimation (a biometric feature for walking pattern recognition), as well as for human pose (e.g. standing or falling down) estimation for potential abnormal behavior and accidental event detection.