Computer
Rashad N. Razak; Hadeel N. Abdullah
Abstract
Multi-Object Detection and Tracking (MODT) are essential in manyapplication fields. Still, many enhancements in the speed of detection and tracking wererequired to overcome the challenges during implementation. This paper presents a newalgorithm system for (MODT) to improve the execution time to be robust ...
Read More ...
Multi-Object Detection and Tracking (MODT) are essential in manyapplication fields. Still, many enhancements in the speed of detection and tracking wererequired to overcome the challenges during implementation. This paper presents a newalgorithm system for (MODT) to improve the execution time to be robust in real-timeapplications. A background subtraction detection algorithm with a Kalman filter wasused to track and predict the object position and speed parameters. To improve theprocessing time, its needs to reduce some frames in a way that does not affect thedetection accuracy too much and instead use the prediction and the estimated valueobtained based on the Kalman filter for the tracked object. This work uses a single videocamera to show how effectively to compute and detect multiple objects concurrently; it isapplied for daytime preprocessing in an automated traffic surveillance system.Preliminary testing findings show that the suggested algorithm for this vehicle monitoringsystem is feasible and effective. It illustrates that using the suggested algorithm with asingle video camera can simultaneously watch, detect, and track several vehicles andimprove execution time. Simulation results on the built system demonstrate that theproposed system reduced the execution time to approximately 41.5% compared to thestandard background subtraction algorithm. Results indicate the proposed algorithm hasan approximate error for the position and speed of detected and tracked objects comparedwith the standard background subtraction algorithm.
