Moving Object Detection

Moving object detection aims to identify objects in motion within a scene, a crucial task across diverse fields from robotics to astronomy. Current research emphasizes robust methods that handle challenging conditions like camera movement, dynamic backgrounds, and varying lighting, often employing convolutional neural networks (CNNs), spiking neural networks (SNNs) on neuromorphic hardware, or graph convolutional networks (GCNNs) for improved accuracy and efficiency. These advancements are improving the accuracy and speed of object detection in real-time applications, impacting fields such as autonomous driving, surveillance, and environmental monitoring. Furthermore, research is exploring the integration of multiple sensor modalities, such as RGB and event cameras, to enhance robustness and performance.