Real-Time Vehicle Detection and Tracking Based on CEEMDAN and LSTM Approach

Abstract

This novel real-time vision system allows for the real-time analysis of color movies recorded by a forward-facing video camera in a moving vehicle on a highway. The system utilizes edge, color, and motion data to detect and track lane lines, road limits, and other vehicles on the road. Car recognition is achieved by matching input data online templates, identifying highway scene characteristics, and evaluating their associations. Vehicle detection can also benefit from temporal differencing and the tracking of motion metrics that are characteristic of cars. Preprocessing, segmentation, feature extraction, and model training are all utilized in the suggested approach. Preprocessing often distorts images and videos captured by cameras in busy public spaces. An algorithm employs the depth image to divide up areas that could be driven in. They used a feature extraction method based on the Haar Wavelet Transform. Once the features have been extracted, CEEMDAN-LSTM is used to train the models. The proposed method outperforms two of the most common alternatives, CEEMDAN and LSTM.