Human motion analysis and tracking are a significant research area in the domain of computer vision. Existing systems of today focus on detection of human targets by analyzing their movements in order to recognize the different activities performed by them. Our research work mainly focuses on using detection and tracking of human targets using a 3D range image camera for surveillance purposes in order to ensure the safety of construction workers and also to monitor their posture and movements for heath related purposes in an active work zone. For this purpose the tracking algorithm proposed performs the segmentation of a human target (i.e. construction worker) from a range image video sequence and then models and tags them in order that their location can be continuously monitored. Unlike most other available systems, our system focuses on using the range or distance information since they indicate how far (in terms of meters) a human target is located away from the camera and more importantly because they are capable of generating a 3D perspective of the human target (i.e. by method of 3D point clouds). The range video sequence is obtained by using a special range image camera, which is an optical imaging system which offers real time 3D image data. Furthermore, the segmented human target is modeled by image skeletonization using a star skeleton structure. This model in future research can be used in conjunction with HMM's (Hidden Markov Models) for human activity recognition. The system designed calculates the angles between different body parts to analyze the posture of a construction worker. It also incorporates the use of a particle filter to trace the path of the construction worker in order to classify different workrelated activities. Our system is also capable of detecting multiple people and tracking each of their paths separately in a given work environment.