Human Body Tracking and Pose Estimation Using Modified Camshift Algorithm

Combining Human Body Shape and Pose Estimation for Robust Upper Body Tracking Using a Depth Sensor

Rapid and accurate estimation of a person’s upper body shape and real-time tracking of the pose in the presence of occlusions is crucial for many future assistive technologies, health care applications and telemedicine systems. We propose to tackle this challenging problem by combining data-driven and generative methods for both body shape and pose estimation. Our strategy comprises a subspace-...

A new approach using Camshift Algorithm for multiple Vehicle Tracking

Cameras and video technology have become integral in our day to day lives. Surveillance is one area that has greatly benefited from video technologies. This in turn increases the need for automatic video surveillance algorithms that can track objects and raise alarm if needed. Tracking of people is one such area. On the other hand, CAMSHIFT is a tracking algorithm that has been widely applied i...

Tracking Using CamShift Algorithm and Multiple Quantized Feature Spaces

The Continuously Adaptive Mean Shift Algorithm (CamShift) is an adaptation of the Mean Shift algorithm for object tracking that is intended as a step towards head and face tracking for a perceptual user interface. In this paper, we review the CamShift Algorithm and extend a default implementation to allow tracking in an arbitrary number and type of feature spaces. In order to compute the new pr...

Tracking with a Novel Pose Estimation Algorithm

Human Motion Tracking and Pose Estimation

Tracking and pose estimation of human figures is a challenging open problem. We focus on model-based approaches to solving this problem, and develop a general framework for describing and comparing the various techniques described in the literature. We briefly describe two existing pose estimation algorithms in terms of this framework: the popular pictorial structures method, and a recent metho...