An intelligent multiple vehicle detection and tracking using modified vibe algorithm and deep learning algorithm

摘要

Multiple vehicle detection is a promising and challenging role in intelligent transportation systems and computer vision applications. Most existing methods detect vehicles with bounding box representation and fail to offer the location of vehicles. However, the location information is vigorous for several real-time applications such as the motion estimation and trajectory of vehicles moving on the road. In this paper, we propose an advanced deep learning method called enhanced you only look once v3 and improved visual background extractor algorithms are used to detect the multi-type and multiple vehicles in an input video. More precisely, tracking is to find the trace of the upcoming vehicles using a combined Kalman filtering algorithm and particle filter techniques. To improve the tracking results, further, we propose the technique, namely multiple vehicle tracking algorithms, and tested with different weather conditions such as sunny, rainy, night and fog in input videos of 30 frames per second. The major research issues were found in the recent kinds of literature in ITS sector which is closely related to the real-time traffic environmental problems such as occlusions, camera oscillations, background changes, sensors, cluttering, camouflage, varying illumination changes in a day- and sunny and at nighttime vision. The experimental results are tested with the ten different input videos and two benchmark datasets KITTI and DETRAC. The most eight high- level features have been considered for automatic feature extraction and annotation. The attributes are length, width, height, number of mirrors and wheels and windscreen shielding glass to detect the target region of interest (vehicles) on road. In addition, further experiments are carried out in multiple-input videos of high definition quality using a monocular camera, and the average accuracy is 98.6%, and the time complexity of the algorithm is O(n) and also tracking results attained 96.6%. The dataset and input videos are discussed in comparative results with the F-test measure done for multiple vehicles.