Background modeling is one of the most challenging and time consuming tasks in motion detection from video sequence. This paper presents a background independent moving object segmentation algorithm utilizing the spatio-temporal information of the last three frames. Existing three-frame based methods face challenges due to the insignificant gradient information in the overlapping region of difference images and edge localization errors. These methods extract scattered moving edges and experience poor detection rate especially when objects with slow movement exist in the scene. Moreover, they are not much suitable for moving object segmentation and tracking. The proposed method solves these problems by representing edges as segments and applying a novel segment based flexible edge matching algorithm which makes use of gradient accumulation through distance transformation. Due to working with three most recent frames, the proposed method can adapt to changes in the environment. Segment based representation facilitates local geometric transformation and thus it can make proper use of flexible matching to provide an effective solution for tracking. To segment the moving object region from the detected moving edges, we introduce a watershed based algorithm followed by an iterative background removal procedure. Watershed based segmentation algorithm helps to extract moving object with more accurate boundary which eventually achieves higher coding efficiency in content based applications and ensures a good visual quality even in the limited bit rate multimedia communication.

This paper presents an automatic edge segment based algorithm for the detection of moving objects that has been specially developed to deal with the variations in illumination and contents of background. We investigated the suitability of the proposed edge segment based moving object detection algorithm in comparison with the traditional intensity based as well as edge pixel based detection methods. In our method, edges are extracted from video frames and are represented as segments using an efficiently designed edge class. This representation helps to obtain the geometric information of edge in the case of edge matching and shape retrieval; and creates effective means to incorporate knowledge into edge segment during background modeling and motion tracking. An efficient approach for background edge generation and a robust method of edge matching are presented to effectively reduce the risk of false alarm due to illumination change and camera motion while maintaining the high sensitivity to the presence of moving object. The proposed method can be successfully realized in video surveillance applications in home networking environment as well as various monitoring systems. As, video coding standard MPEG-4 enables content based functionality, it can successfully utilize the shape information of the detected moving objects to achieve high coding efficiency. Experiments with real image sequences, along with comparisons with some other existing methods are presented, illustrating the robustness of the proposed algorithm.

This paper presents a new routing approach to extend the effective lifetime of mobile ad hoc networks (MANET) considering both residual battery energy of the participating nodes and routing cost. As the nodes in ad hoc networks are limited in power, a power failure occurs if a node has insufficient remaining energy to send, receive or forward a message. So, it is important to minimize the energy expenditure as well as to balance the remaining battery power among the nodes. Cost effective routing algorithms attempt to minimize the total power needed to transmit a packet which causes a large number of nodes to loose energy quickly and die. On the other hand, lifetime prediction based routing algorithms try to balance the remaining energies among the nodes in the networks and ignore the transmission cost. These approaches extend the lifetime of first few individual nodes. But as nodes spend more energy for packet transfer, power failures occurs within short interval resulting more number of total dead node earlier. This reduces the effective lifetime of the network, as at this stage successful communication is not possible due to the lack of forwarding node. The proposed method keeps the transmission power in modest range and at the same time tries to reduce the variance of the residual energy of the nodes more effectively to obtain the highest useful lifetime of the networks in the long run. Nonetheless, movement of nodes frequently creates network topology changes via link breaks and link creation and thus effects on the stability of the network. So, the pattern of the node movement is also incorporated in our route selection procedure.