Research on Object Detection and Tracking based on Deep Learning

With the advancement of artificial intelligence technology, object detection technology in the field of computer vision has played a key role. This article aims to address the accuracy and speed of existing methods in processing live video streams. With the development of deep learning technology, we propose a novel framework of convolutional neural network (CNN) architecture, which optimizes the process of feature extraction and object classification, and significantly improves the detection accuracy. In addition, we have integrated recurrent neural networks (RNNs) to improve the tracking continuity of targets in video sequences. Through the fusion of these technologies, our model not only performs well in multi-target detection, but also reliably tracks targets in the case of occlusion and fast movement. Experimental results show that compared with the existing deep learning methods, the performance of our model on the standard dataset is significantly improved, with a 20% increase in detection speed and a 15% increase in accuracy.