Computer vision is a very important research direction in the cognitive computing field. Robots encounter various target-tracking problems with computer vision systems. Robust scale estimation is an important issue in tracking algorithms. Most of the available methods have difficulty addressing even reasonable changes of scale in complex videos. In this paper, we propose a visual tracking method based on robust scale estimation, which uses a discriminant correlation filter based on a time-dependent scale-space filter and an adaptive cross-correlation filter. The tracker uses separate essential filters for sample migration and scale estimation. Furthermore, the built-in scale estimation method can be introduced into other tracking algorithms. We validate the proposed method on the UAV123 dataset. The results of comparison experiments with the traditional correlation filter tracking method demonstrate that the proposed method improves the success rate and tracking accuracy while controlling the computational complexity; its success rate measured by the area under the curve is 0.638, while at a location error precision of 20%, it is 0.649.

The mobile wireless sensor networks have been used in many popular applications. However, the limited battery capacity of sensor nodes is still one of the key issues in wireless sensor networks (WSNs). The new and effective way is to use wireless energy transfer and rechargeable lithium batteries to solve this problem. To improve the charging energy effectiveness, we introduce a stop-wait scheme that allows the mobile charger (MC) to stop and wait to charge the coming mobile sensors. The lifetime of the WSNs is considered to assure that each sensor will not run out of energy. The simulation experiment shows that our algorithm improves the energy usage effectiveness.

In mobile wireless multimedia sensor networks (MWMSNs) image compression task coordination, the existing methods without considering the dynamic changes of the processing ability and location of cooperative nodes, which will cause frequent interruptions of image compression tasks and task data re-transmission. To solve these problems, an image compression task cooperation algorithm based on dynamic alliance (ATDA) is proposed. The image compression task is divided into image transfer subtask and image compression subtask based on the task stable execution time and the principles and constraints of task decomposition. Simulation results show that the proposed algorithm can realize the task load balancing of the alliance collaboration nodes and reduce the execution time of the image compression task and energy consumption.