Temporal Semantics Auto-Encoding based Moving Objects Detection in Urban Driving Scenario
Fahad Lateef, Mohamed Kas, Yassine Ruichek · 2021
Detecting moving objects from a moving vehicle is a challenging problem and crucial for autonomous driving, especially in urban scenarios. The current literature has focused on this task as many approaches have been dedicated to moving object detection. These approaches consist of multistage pipelines, including semantic segmentation and optical flow estimation, and require multiple sources of information from active and passive sensors. However, they fail to accurately segment moving objects due to the large ego- camera motion, in addition to the high processing time. In this work, we propose a novel approach to moving object detection by processing information only from a camera. Our approach is based on integrating an encoder-decoder network (EDNet) with a semantic segmentation model (Mask R-CNN), where Mask R-CNN detects the objects of interest and the EDNet classifies their motion (moving/static) over two consecutive frames. We compare the results of our proposed model with existing MOD models on three SOTA benchmarks. We achieved SOTA performance in terms of visual quality and accuracy with competitive speed.