A comparative study of real-time object detection using tensorflow's single shot multibox detector (SSD) and histogram of oriented gradient (HOG).

This study's main goal is to employ a unique Single Shot Multibox identification (SSD) algorithm to predict item identification with a greater rate of accuracy than the Histogram of Oriented Gradient (HOG) approach. Supplies and Procedures: To enhance object detection prediction, a sample of twenty participants was split into two groups of 10 individuals each. The calculation made use of a 95% confidence level, an alpha and beta proportion of 0.05 and 0.2, and a G worth of 0.8. Similar numbers of information tests (N=10) were subjected to both the SSD and Hoard approaches; SSD provided a greater level of accuracy. Findings: The suggested SSD computation has a success rate of 93.47%, whereas the Hoard classifier's rate is 90.43%. The independent sample T-test results in the p-value 0.000 (P0.05), which is less than the 0.05 significance level. This illustrates the statistical importance of the results. Conclusion: SSD model shows higher accuracy in comparison to the Hoard model, for item recognition predictions. [ABSTRACT FROM AUTHOR]