In recent years, dynamic vision sensors (DVS), also known as event-based cameras or neuromorphic sensors, have seen increased use due to various advantages over traditional frame-based cameras. Its high temporal resolution overcomes motion blurring, its high dynamic range overcomes extreme illumination conditions and its low power consumption makes it suitable for platforms such as drones and self-driving cars. While frame-based computer vision is mature due to the large amounts of data and ground truth available, event-based computer vision is still a work in progress as data sets are not as aplenty and ground truth is scarce for tasks such as object detection. In this paper, we suggest a way to overcome the lack of labeled ground truth by introducing a simple method to generate pseudo-labels for dynamic vision sensor data, assuming that the corresponding frame-based data is also available. These pseudo-labels can be treated as ground truth when training on supervised learning algorithms, and we show, for the first time, event-based car detection under ego-motion in a realistic environment at 100 frames per second with an average precision of 41.7 relative to pseudo-ground truth.
This paper reports an object tracking algorithm for a moving platform using the dynamic and active-pixel vision sensor (DAVIS). It takes advantage of both the active pixel sensor (APS) frame and dynamic vision sensor (DVS) event outputs from the DAVIS. The tracking is performed in a three step-manner: regions of interest (ROIs) are generated by a cluster-based tracking using the DVS output, likely target locations are detected by using a convolutional neural network (CNN) on the APS output to classify the ROIs as foreground and background, and finally a particle filter infers the target location from the ROIs. Doing convolution only in the ROIs boosts the speed by a factor of 70 compared with full-frame convolutions for the 240×180 frame input from the DAVIS. The tracking accuracy on a predator and prey robot database reaches 90 with a cost of less than 20ms frame in Matlab on a normal PC without using a GPU. This paper describes the application of a Convolutional Neural Network (CNN) in the context of a predator prey scenario. The CNN is trained and run on data from a Dynamic and Active Pixel Sensor (DAVIS) mounted on a Summit XL robot (the predator), which follows another one (the prey). The CNN is driven by both conventional image frames and dynamic vision sensor "frames" that consist of a constant number of DAVIS ON and OFF events. The network is thus "data driven" at a sample rate proportional to the scene activity, so the effective sample rate varies from 15 Hz to 240 Hz depending on the robot speeds. The network generates four outputs: steer right, left, center and non-visible. After off-line training on labeled data, the network is imported on the on-board Summit XL robot which runs jAER and receives steering directions in real time. Successful results on closed-loop trials, with accuracies up to 87 or 92 (depending on evaluation criteria) are reported. Although the proposed approach discards the precise DAVIS event timing, it offers the significant advantage of compatibility with conventional deep learning technology without giving up the advantage of data-driven computing.