Efficient Depth Map Estimation Method Based on Gradient Weight Cost Aggregation Strategy for Distributed Video Sensor Networks

Video sensor networking technologies have developed very rapidly in the last ten years. In this paper, a cross-based framework strategy for cost aggregation is presented for the depth map estimation based on video sensor networks. We formulate the process as a local regression problem consisting of two main steps with a pair of video sensors. The first step is to calculate estimates for a set of points within a shape-adaptive local support region. The second step is to aggregate the matching cost for the gradient-based weight of the support region at the outmost pixel. The proposed algorithm achieves strong results in an efficient manner using the two main steps. We have achieved improvement of up to 6.9%, 8.4%, and 8.3%, when compared with adaptive support weight (ASW) algorithm. Comparing to cross-based algorithm, the proposed algorithm gives 2.0%, 1.3%, and 1.0% in terms of nonocclusion, all, and discontinuities, respectively. 1. Introduction Wireless sensor networks (WSN) have drawn the attention of the research community in the last few years, driven by a wealth of theoretical and practical applications [1, 2]. Recently, as rapid improvements and miniaturization in hardware, a single embedded device can be equipped with audio and visual information collection modules [3]. The availability of low-cost hardware is like enabling the development of wireless multimedia sensor networks (WMSNs), that is, networks of resource-constrained wireless devices that can retrieve multimedia content such as video and audio streams, still images, and scalar sensor data from the environment [4–7]. There are many algorithms for developing WMSNs applications [8–12]. In [8], Chi et al. have studied the problem of compression of video surveillance sequences collected by a wireless sensor network. In particular, they have proposed a low-complexity coding framework based on change detection and JPEG-like compression of regions of interest, along with a suitable low-complexity change detection algorithm. Huang et al. have proposed a robot wireless sensor network that can enhance multimedia surveillance and provide the foundation for strategies based on multi-modal sensor integration [9]. In [10], DeBardelaben have investigated techniques that can be applied at each layer of the network protocol stack to produce clandestine, power-efficient wireless microsensor network implementations. Also, a smart camera network has been demondtrated for providing extensive coverage of a large virtual public space, a train station populated by autonomously self-animating virtual