Modify Multiple Object Detection and Tracking to Improve the Execution Time

Multi-Object Detection and Tracking (MODT) is crucial in various contexts. Despite this, significant advancements in detection and tracking speed were needed to meet the challenge during the implementation phase. Introduce a revised algorithmic framework for (MODT) to speed up processing and make it more stable for use in real-time settings. The object's position and velocity were predicted using a Kalman filter and a background subtraction detection approach. When the detector isn't actively searching for a new object, it can be beneficial to discard the successive two frames and replace them with the Kalman filter's prediction and estimated value for the monitored object to speed up the process. Adding some image filter-like aspect ratio object and motion which help to reduce the effectiveness of the shadow and variations of the lighting conditions in the scene, which improve the proposed algorithm detection and tracking, This is useful for daytime preprocessing in an automated traffic surveillance system and inside pedestrian monitoring, and it can be shown with the help of a video camera. The results of these preliminary tests indicate that the proposed algorithm for this vehicle monitoring system works. It demonstrates that when applied to a single camera, the proposed method can monitor, detect, and track many vehicles or human being simultaneous, with improved execution time by 22% over the standard background subtraction and tolerable complexity.