Improve Multi-Object Detection and Tracking for an Automated Traffic Surveillance System

Abstract

Multi-Object Detection and Tracking (MODT) are essential in many application fields. Still, many enhancements in the speed of detection and tracking were required to overcome the challenges during implementation. This paper presents a new algorithm system for (MODT) to improve the execution time to be robust in real-time applications. A background subtraction detection algorithm with a Kalman filter was used to track and predict the object position and speed parameters. To improve the processing time, its needs to reduce some frames in a way that does not affect the detection accuracy too much and instead use the prediction and the estimated value obtained based on the Kalman filter for the tracked object. This work uses a single video camera to show how effectively to compute and detect multiple objects concurrently; it is applied for daytime preprocessing in an automated traffic surveillance system. Preliminary testing findings show that the suggested algorithm for this vehicle monitoring system is feasible and effective. It illustrates that using the suggested algorithm with a single video camera can simultaneously watch, detect, and track several vehicles and improve execution time. Simulation results on the built system demonstrate that the proposed system reduced the execution time to approximately 41.5% compared to the standard background subtraction algorithm. Results indicate the proposed algorithm has an approximate error for the position and speed of detected and tracked objects compared with the standard background subtraction algorithm. Index Terms— multi-object detection, tracking, background subtraction, Kalman filter, morphological.