Data Association Methods via Video Signal Processing in Imperfect Tracking Scenarios: A Review and Evaluation

In 5G scenarios, there are a large number of video signals that need to be processed. Multiobject tracking is one of the main directions in video signal processing. Data association is a very important link in tracking algorithms. Complexity and efficiency of association method have a direct impact on the performance of multiobject tracking. Breakthroughs have been made in data association methods based on deep learning, and the performance has been greatly improved compared with traditional methods. However, there is a lack of overviews about data association methods. Therefore, this article first analyzes characteristics and performance of three traditional data association methods and then focuses on data association methods based on deep learning, which is divided into different deep network structures: SOT methods, end-to-end methods, and Wasserstein metric methods. The performance of each tracking method is compared and analyzed. Finally, it summarizes the current common datasets and evaluation criteria for multiobject tracking and discusses challenges and development trends of data association technology and data association methods which ensure robust and real time need to be continuously improved.

A simultaneous decomposition for three quaternion tensors with applications in color video signal processing

In this paper, we establish a simultaneous decomposition for three quaternion tensors via Einstein product. This simultaneous decomposition transforms the given three quaternion tensors into nice forms which have only 1 and 0. We conclude with an application in the color video signal processing. This new approach only need to store four keys to realize the simultaneous encryption and decryption of three videos.

Implementation challenge and analysis of thermal image degradation on R-CNN face detection

Face detection systems with color cameras were rapidly evolving and have been well researched. In environments with good visibility they can reach excellent accuracy. But changes in illumination conditions can result in performance degradation, which is the one of the major limitations in visible light face detection systems. The solution to this problem could be in using thermal infrared cameras, since their operation doesn't depend on illumination. Recent studies have shown that deep learning methods can achieve an impressive performance on object detection tasks, and face detection in particular. The goal of this paper is to find an effective way to take advantages from thermal infrared spectra and provide an analysis of various image degradation influence on thermal face detection performance in a system based on R-CNN with special accent on implementation on a hardware platform for video signal processing that institute Vlatacom has developed, called vVSP.