Sunday 23 March 2025
Researchers have made significant strides in developing a bioinspired approach to visual attention, which could have major implications for robotics and computer vision.
The human brain has an incredible ability to focus on specific objects or regions of interest while ignoring the rest of the visual scene. This is known as selective attention, and it’s essential for tasks like recognizing faces, reading text, or navigating through cluttered environments. In order to replicate this capability in machines, scientists have been working on developing algorithms that can mimic the way our brains process visual information.
One key component of visual attention is object motion sensitivity. The ability to detect and track moving objects is crucial for many applications, including autonomous vehicles, surveillance systems, and robotic grasping tasks. However, traditional computer vision approaches often rely on complex and computationally expensive algorithms, which can be a major limitation in real-world scenarios where processing power is limited.
To address this challenge, researchers have been exploring the use of event-based cameras, which capture asynchronous changes in the visual scene rather than generating a continuous stream of images. This approach has several advantages, including reduced bandwidth requirements and lower latency. However, it also requires new algorithms that can effectively process and analyze the sparse and asynchronous data generated by these cameras.
The team behind this latest research has developed a spiking neural network (SNN) that can learn to detect moving objects using event-based cameras. The SNN is inspired by the biology of the mammalian retina, which is capable of detecting object motion through the movement of photoreceptors and horizontal cells. By mimicking this process, the researchers were able to create an algorithm that can accurately detect and track moving objects in real-time.
The system consists of a Dynamic Vision Sensor (DVS) integrated into a Speck neuromorphic hardware platform, which is mounted on a pan-tilt unit to simulate eye movements. The DVS generates events when it detects changes in the visual scene, which are then processed by the SNN. The network uses a combination of feedforward and feedback connections to learn object motion patterns and adapt to changing environments.
The results are impressive: the system is able to detect moving objects with an accuracy of 88.8% in office scenarios and 89.8% in challenging indoor and outdoor low-light conditions. Additionally, the detection time is remarkably fast, taking only 0.124 seconds to identify a salient object in dynamic scenes.
This research has significant implications for robotics and computer vision.
Cite this article: “Bioinspired Visual Attention for Robotics and Computer Vision”, The Science Archive, 2025.
Visual Attention, Bioinspired Approach, Selective Attention, Object Motion Sensitivity, Event-Based Cameras, Spiking Neural Network, Snn, Dynamic Vision Sensor, Dvs, Neuromorphic Hardware, Robotics, Computer Vision.
