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Person on foot discovery is one of the significant errands in item location innovation. The person on foot identification calculation has been utilized in applications like shrewd video reconnaissance, traffic examination, and self-governing driving. Lately, numerous passer by discovery calculations have been proposed however the key downside is the precision and speed, which can be improved my coordinating productive calculations. The proposed model improves the person on foot identification calculation by incorporating two proficient calculations together. The model is created utilizing the joint rendition of ResNet and YOLO v2, which preforms highlight extraction and grouping separately. By utilizing this model, the effectiveness of the framework is expanded by improving the precision rate so it tends to be utilized with constant applications. The model has been contrasted and existing models like SSD, Faster R-CNN and Mask R-CNN. Contrasting and these models, the proposed model gives mAP esteem higher than these current models with less misfortune work when tried on the INRIA dataset.