The sparse representation classification method has been widely concerned and studied in pattern recognition because of its good recognition effect and classification performance. Using the minimized l 1 norm to solve the sparse coefficient, all the training samples are selected as the redundant dictionary to calculate, but the computational complexity is higher. Aiming at the problem of high computational complexity of the l 1 norm based solving algorithm, l 2 norm local sparse representation classification algorithm is proposed. This algorithm uses the minimum l 2 norm method to select the local dictionary. Then the minimum l 1 norm is used in the dictionary to solve sparse coefficients for classify them, and the algorithm is used to verify the gesture recognition on the constructed gesture database. The experimental results show that the algorithm can effectively reduce the calculation time while ensuring the recognition rate, and the performance of the algorithm is slightly better than KNN-SRC algorithm.

Camera calibration is a crucial problem in many applications, such as 3D reconstruction, structure from motion, object tracking and face alignment. Numerous methods have been proposed to solve the above problem with good performance in the last few decades. However, few methods are targeted at joint calibration of multi-sensors (more than four devices), which normally is a practical issue in the real-time systems. In this paper, we propose a novel method and a corresponding workflow framework to simultaneously calibrate relative poses of a Kinect and three external cameras. By optimizing the final cost function and adding corresponding weights to the external cameras in different locations, an effective joint calibration of multiple devices is constructed. Furthermore, the method is tested in a practical platform, and experiment results show that the proposed joint calibration method can achieve a satisfactory performance in a project real-time system and its accuracy is higher than the manufacturer’s calibration.

Camera calibration is a process of estimating intrinsic parameters and extrinsic parameters of camera [1]. It makes the measurement of distances in a real world from their projections on the image plane possible [2]. Thus, With the continuous development of computer/machine vision, camera calibration is believed to be widely applied in 3D reconstruction [3,4], structure from motion [5], object tracking [6-8] and gesture recognition [9,10], etc. With the development of computer vision, more and more cameras that can acquire 3D information have been proposed, such as stereo cameras and Time of Flight (TOF) cameras. On 4 November 2010, With the launch of low-cost Microsoft Kinect sensors, 3D depth cameras are increasingly attracting researchers due to their versatile applications in computer vision [11]. Kinect was originally developed to improve the game player's experience, enhance human-computer interaction, it actually an RGB-D sensor which provides Synchronized RGB color and depth images. The image capture device of the Kinect include a color camera and a depth sensor which consists of the infrared (IR) projector combined with the IR camera. The experimental results show that Kinect was more accurate than the TOF depth sensor, and close to a medium-resolution stereo camera. However, it is well known that these parameters vary from device to device, and that the factory presets are not accurate enough for many applications [12]. To deal with the above issue, N. Burrus [13] obtained basic Kinect calibration algorithms by using

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