## 1 Introduction  
The development of three-dimensional (3D) human skeleton extraction contributes enormously to prospering fields like virtual reality and somatosensory human-computer interaction. However, current 3D human skeletonization algorithms require specified acquisition equipments including RGB-Depth (RGB-D) cameras and wearable sensors, or a specific experimental setup like ring illuminator array. RGB-D cameras like Microsoft Kinect are designed to perform human skeletonization in a short range [1; 2]. Wearable sensors only perform effective skeletonization on human subjects wearing experimental tags. Ring illuminator array requires precise subject position and illuminator array setup during the 3D modelling and skeletonization procedures. These setup restrictions of traditional human skeletonization methods bring great limitation on the outdoor applications. Instead of deploying algorithms on traditional specified platforms, this work pays attention to the commonest projection of the human body on the ground. Shadow is the projection of a opaque object on a certain surface, containing single-view silhouette information of the object. Multiple methods have been developed to extract information from shadow. Current methods mainly focus on the recovery of mesh model [3; 4; 5] or point clouds [6; 7] of static objects based on partial shadow information [8]. In this paper, a silhouetted shadow-based skeleton extraction (SSSE) method is proposed. The proposed SSSE method deploys shadow information extraction algorithm to the field of human skeletonization [9; 10; 11]. Based on the proposed SSSE method, six 3D joint positions in the human skeleton can be precisely extracted in outdoor scenarios with a normal monocular camera. Compared with current indoor 3D human skeleton extraction methods based on RGB-D cameras like Kinect, the proposed SSSE method reduces constraints on input device choice and application environment setup. This work is motivated by the procedure of taking a silhouette photo. During this procedure, the human body blocks a part of light from reaching film or sensor, leaving a body sketch on the silhouette photo. Human shadow on the ground, from the aspect of silhouette imaging, can be regarded as a silhouette photo of the human body on a special giant film. The ground surface plays the role of film. For captured frames containing human shadows, each shadow on the ground can provide extra human contour information from a unique observation angle view other than the camera view. This paper mainly focuses on the extraction and aggregation of the extra silhouette information from spatial-temporal discrete human shadows on the ground, aiming to perform 3D human skeletonization with a monocular camera in outdoor scenarios. Based on the aggregation of multiple shadows from discrete spatial-temporal coordinates, SSSE is capable of launching 3D human skeletonian even in outdoor scenes where the scale is too large for traditional methods [12; 13; 14] to handle [6; 15; 16]. The main contributions of this paper are related to three aspects:  
1. The 3D human skeletonization is realized with a normal monocular camera based on the proposed SSSE method. 2. The proposed SSSE method achieves 3D human skeletonization in a large-scale outdoor scene. 3. The proposed SSSE method deploys the aggregation of temporal-spatial discrete two-dimensional (2D) shadow information in a 3D human skeletonization procedure  
The remaining sections of this paper are organized as follows: In Section 2, the basic theory for shadow-based single frame human skeletonization is introduced first, followed by the advanced SSSE method aggregating temporal-spatial discrete shadow information to recover complete skeleton sequences. In Section 3, a five-step method is introduced to deploy the proposed SSSE method in large-scale outdoor scenarios with a monocular camera. In Section 4, the effective range and precision of the SSSE skeletonization results are evaluated in comparison with the skeletonization result of traditional RGB-D method.