Research on the improvement of vision target tracking algorithm for Internet of things technology and Simple extended application in pellet ore phase

With the extensive application of Internet of things technology and visual object tracking technology in various neighborhoods, the traditional visual object tracking algorithm can no longer meet the needs of Internet of things. The Internet of things with its powerful perception, multi-information transmission ability and super intelligent processing ability force the traditional visual object tracking algorithm to innovate and improve. In order to improve the tracking algorithm’s ability to cope with complex and drastic changes in the target’s appearance, in this paper, an accurate and robust visual object tracking algorithm (visual object tracking) based on K-means clustering algorithm on the integrated model ENS is studied. The tracking target is divided into several sample packets, and the size of the sample packet is compressed to match different weights of the filter in frequency domain. Finally, a more accurate and robust filter is built to realize the tracking of the target. Experimental simulation results show that the improved visual object tracking algorithm ENS-CSK approximately conforms to two-dimensional gaussian distribution in tracking effect, and the tracking success rate and accuracy are higher than the AdaBoost algorithm and KCF algorithm mentioned in the paper. Therefore, the improved visual object tracking algorithm ENS-CSK algorithm in this paper can better match the visual object tracking under the Internet of things technology. Therefore, it can be extended to the process of ore phase transformation of pellet under continuous calcination with varying temperature. Thus, the pressure resistance of pellets at different calcining temperatures can be predicted more accurately.