Geo-Temporal Selective Approach for Dynamic Depth Estimation in Outdoor Object Detection and Distance Measurement

Abstract

Accurate depth information is crucial for various computer vision applications such as augmented reality, 3D modeling, and autonomous vehicles. Recent advancements have significantly improved both supervised and self-supervised methods for depth estimation. However, most current approaches primarily focus on monocular depth estimation and face challenges in overcoming quality limitations due to the inherent constraints of supervised learning in deep neural networks. Incorporating temporal information from sequential frames can enhance the quality of these methods. This paper explores innovative methods for integrating recurrent blocks into existing pipelines for supervised depth estimation using convolutional long short-term memory (convLSTM). By utilizing convLSTM, we can capture a wealth of valuable information. To accurately measure object distances, we employed a geospatial approach. After extensive analysis of training methods, new deep neural network architectures have been designed specifically for monocular video depth estimation. Our research emphasizes using an attention mechanism to extract information from previous frames. The proposed approach effectively measures both long and short-range object distances by combining geospatial and temporal mechanisms, demonstrating superior performance in monocular depth estimation tasks.

Keywords

• Depth estimation
• convLSTM
• depth map
• temporal information
• frame sequences