Pedestrian detection with high-resolution event camera
This work addresses perception challenges for autonomous vehicles like drones and self-driving cars, but it is incremental as it compares existing methods on new event camera data.
The paper tackled pedestrian detection using high-resolution event cameras to address issues like motion blur and low frame rates in traditional cameras, comparing deep learning methods and achieving results that illustrate the potential of event cameras with evaluations of accuracy and efficiency on 1280 x 720 pixel footage.
Despite the dynamic development of computer vision algorithms, the implementation of perception and control systems for autonomous vehicles such as drones and self-driving cars still poses many challenges. A video stream captured by traditional cameras is often prone to problems such as motion blur or degraded image quality due to challenging lighting conditions. In addition, the frame rate - typically 30 or 60 frames per second - can be a limiting factor in certain scenarios. Event cameras (DVS -- Dynamic Vision Sensor) are a potentially interesting technology to address the above mentioned problems. In this paper, we compare two methods of processing event data by means of deep learning for the task of pedestrian detection. We used a representation in the form of video frames, convolutional neural networks and asynchronous sparse convolutional neural networks. The results obtained illustrate the potential of event cameras and allow the evaluation of the accuracy and efficiency of the methods used for high-resolution (1280 x 720 pixels) footage.