Moving Object Detection in Traffic Surveillance Video: New MOD-AT Method Based on Adaptive Threshold

Abstract

Previous research on moving object detection in traffic surveillance video has mostly adopted a single threshold to eliminate the noise caused by external environmental interference, resulting in low accuracy and low efficiency of moving object detection. Therefore, we propose a moving object detection method that considers the difference of image spatial threshold, i.e., a moving object detection method using adaptive threshold (MOD-AT for short). In particular, based on the homograph method, we first establish the mapping relationship between the geometric-imaging characteristics of moving objects in the image space and the minimum circumscribed rectangle (BLOB) of moving objects in the geographic space to calculate the projected size of moving objects in the image space, by which we can set an adaptive threshold for each moving object to precisely remove the noise interference during moving object detection. Further, we propose a moving object detection algorithm called GMM_BLOB (GMM denotes Gaussian mixture model) to achieve high-precision detection and noise removal of moving objects. The case-study results show the following: (1) Compared with the existing object detection algorithm, the median error (MD) of the MOD-AT algorithm is reduced by 1.2–11.05%, and the mean error (MN) is reduced by 1.5–15.5%, indicating that the accuracy of the MOD-AT algorithm is higher in single-frame detection; (2) in terms of overall accuracy, the performance and time efficiency of the MOD-AT algorithm is improved by 7.9–24.3%, reflecting the higher efficiency of the MOD-AT algorithm; (3) the average accuracy (MP) of the MOD-AT algorithm is improved by 17.13–44.4%, the average recall (MR) by 7.98–24.38%, and the average F1-score (MF) by 10.13–33.97%; in general, the MOD-AT algorithm is more accurate, efficient, and robust.

Vehicle Detection Using Machine Learning Model with the Gaussian Mixture Model (GMM)

“…The results showed that IoT-based technology and modified techniques for taking out the background worked well together. By applying GMM Filter [22,23] to reduce unwanted small items, this research completes and continues object detection on video.…”

“…Figure 2: Proposed ModelGaussian Mixture Model (GMM) is one of the ways to get rid of the background. GMM models the time series of pixel values[23][24][25]. The accuracy of the Gaussian Mixture Models (GMM) method in separating the background and the tracked object can be evaluated using several metrics, such as: a. Intersection over Union (IoU): This metric measures the overlap between the predicted object bounding box and the ground truth bounding box, divided by their union.…”

Background subtraction challenges in motion detection using Gaussian mixture model: a survey

“…The image size of motion detection can vary due to the effects of camera imaging characteristics, and the noise can easily influence the findings of motion detection. Because of this, the precision of the identification of moving objects is easily compromised [27], [28]. The presence of noise in an image will not only have an impact on how the image appears to the human eye, but it will also have an impact on the following processing of the image, such as the extraction of image features, the categorization, and recognition of images, and so on.…”

“…Luo et al [27] used a method for detecting motion that considers the variation in the spatial image threshold. The researcher calculates the projected size of motion in the image regions by establishing the mapping relationship between the geometric features of motion in the image regions and the reasonable level circumscribed rectangle (BLOB) of motion in the geographic space.…”