Automation Of Video Surveillance Is Gaining Extensive Interest Recently, Considering The Public Security Issues. In Recent Times, A Systematic And Exact Detection Of An Object Is A Foremost Point In Computer Vision Technology. With The Unfolding Of Recent Deep Learning Techniques, The Precision Of Detecting An Object Has Increased Greatly Thereby Igniting The Interest In This Area To Large Extent. Also, With The Advent Of Automatic Driving Electric Cars, The Accurate Detection Of Objects Has Gained Phenomenal Importance. The Main Aim Is To Integrate The State Of-the-art Deep Learning Method On Pedestrian Object Detection In Real-time With Improved Accuracy. One Of The Crucial Problems In Deep Learning Is Using Computer Vision Techniques, Which Tend To Slow Down The Process With Trivial Performance. In This Work, An Improved Yolov3 Transfer Learning-based Deep Learning Technique Is Used For Object Detection. It Is Also Shown That This Approach Can Be Used For Solving The Problem Of Object Detection In A Sustained Manner Having The Ability To Further Separate Occluded Objects. Moreover, The Use Of This Approach Has Enhanced The Accuracy Of Object Detection. The Network Used Is Trained On A Challenging Data Set And The Output Obtained Is Fast And Precise Which Is Helpful For The Application That Requires Object Detection.
