Improving Visual Object Detection using General UFIR Filtering

Object detection is a fundamental task in computer vision, which involves the identification and localization of objects within image frames or video sequences. The problem is complicated by large variations in the video camera bounding box, which can be thought of as colored measurement noise (CMN). In this paper, we use the general unbiased finite impulse response (GUFIR) approach to improve detection performance under CMN. The results are compared to the general Kalman filter (GKF) and two detection methods: “Faster-RCNN” and “Tensorflow PASCAL Visual Object Classes (VOC)”. Experimental testing is carried out using the benchmark data ”Car4”. It is shown that GUFIR significantly improves the detection accuracy and demonstrates the properties of the effective tool for visual object tracking.