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This work does a comparative studyon how hyperparameter configurationinfluence on the Histogram of oriented gradient technique used for detecting humans in the videos or images. We used OpenCV implementation of technique developed by dalal & triggs to analyze human detection for real-time system. In this paper, we presenta study regarding the influence of hyperparameter configuration in classification accuracy, depending on the user and the activities performed by each user. Human detection with higher accuracy can be done by using deep neural networks, CNN but that needs heavy computations, hardware and still take time to process as these include 150 network layers and millions of parameters, sometimes need big GPUs to process these features. We need algorithms that can easily deploy on edge devices so we did analysis on classical computer vision techniqueHistogram of oriented gradient and combine it with Machine learning tool i.e Support Vector Machines for predicting humans in frame. The algorithm is run through a common data set(MIT and INRIA pedestrian dataset). All the conclusion derived, is based on continuously taking readings from the given algorithms. Also, the algorithm is chosen in such a manner that it can stand against modern ANN based detection in real time systems, which makes it explicit compared to the work existing in this field. The data recorded from the execution is analyzed based on the previous work on this technique.