A Comprehensive Review of Deep Learning Techniques for Image and Video Analysis

This paper provides a thorough review of deep learning techniques in image and video analysis, focusing on multi-object tracking, convolution and recurrent neural networks, image matting, video recognition, and applications such as object detection. It explores challenges like interpretability and ethical concerns, suggesting future directions such as adversarial robustness and edge computing. The article emphasizes the interdisciplinary nature of the field and advocates for collaborative efforts between academia and industry. Overall, it serves as a valuable resource for researchers and practitioners in the dynamic realm of deep learning for visual data processing.