Stereoscopic system for human body tracking in natural scenes

Abstract

Human body detection and tracking in a scene constitutes a very active working field due to their applicability to many areas, specially as a man-machine interface (MMI) means. The system presented aims to improve the reliability and efficiency of teleoperation. The system is of application to teleoperated manipulation in civil applications such as big robots in shipyards, mines, public works or even cranes. Image segmentation is performed from movement detection. The recognition of moving bodies is verified by means of a simplified articulated cylindrical model, thus allowing to operate with a low computational cost.

A Survey of Computer Vision-Based Human Motion Capture

“…Subtraction is widely used by simply subtracting the current image from the previous image in a pixel by pixel fashion, using either the intensity values [120] or the gradients [4]. An improved version is to use three consecutive images instead of two [78].…”

“…The information may be templates [132], transformed templates [113], normalized silhouettes [52], or postures [22]. The goal of static recognition is mainly to recognize various postures, e.g., pointing [74], standing and sitting [6], or specially defined postures used in interfaces [4,41].…”

“…Such methods vary from correlation [12] and silhouette matching [32] to HMMs [17] and neural networks [54]. The objective is to recognize actions such as walking [123], carrying objects [51], removing and placing objects [96], pointing and waving [32], gestures for control [4], standing vs walking [53], walking vs jogging [115], walking vs running [43], and classifying various aerobic exercises [122] or ballet dance steps [21].…”

Motion Segmentation using Mathematical Morphology

“…Combinations of the four approaches above have been tried, for instance, in [4], the authors use a hybrid approach mixing temporal differentiation and background subtraction obtaining good results; in [1], the authors use temporal differentiation between the gradient [9] of two consecutive frames and a 3D human model is fitted into the result (to obtain a real-time performance, specialized hardware has been designed and used).…”

Segmentação de movimento usando morfologia matemática

“…Em [11,44], por exemplo, os autores utilizam um algoritmo que mistura a diferenciação temporal e a subtração de fundo obtendo bons resultados. Em [2], a diferenciação temporal foi adaptada para ser utilizada com o gradiente [24] de dois quadros consecutivos e um modelo humano 3Dé encaixado ao resultado (utiliza-se hardware especializado para obtenção de resposta em tempo real). Em [50], usa-se uma combinação de fluxoóptico e subtração de fundo para a segmentação do alvo.…”