Object Detection in Video Frames using Deep Learning

摘要

The object detection based on deep learning is an important application like scene understanding, video surveillance, robotics, self-driving systems etc. in deep learning method which is eminent by its strong capability of feature learning and feature representation compared with the traditional object detection methods. With the rapid development in deep learning, more powerful tools, able to learn semantic, highlevel, deeper features, are introduced to address the problems existing in traditional architectures. These models perform differently in network architecture, training strategy and optimization function. This paper introduces the classical methods for object detection and illustrates the relation and difference between the classical methods and the deep learning methods for object detection. Moreover, it introduces the appearance of the object detection based on deep learning elaborates the most typical methods nowadays via deep learning. The paper focuses on the framework design and the working principle of the models and examines the model performance in the real-time environment and hence for the accuracy of object detection. Furthermore, a survey of several specific tasks including salient object detection features, face detection and pedestrian detection has also been briefly discussed. Finally, the main challenges in object detection using deep learning and some solutions for reference has been discussed.