Experimental Comparison between Event and Global Shutter Cameras

Abstract

We compare event-cameras with fast (global shutter) frame-cameras experimentally, asking: “What is the application domain, in which an event-camera surpasses a fast frame-camera?” Surprisingly, finding the answer has been difficult. Our methodology was to test event- and frame-cameras on generic computer vision tasks where event-camera advantages should manifest. We used two methods: (1) a controlled, cheap, and easily reproducible experiment (observing a marker on a rotating disk at varying speeds); (2) selecting one challenging practical ballistic experiment (observing a flying bullet having a ground truth provided by an ultra-high-speed expensive frame-camera). The experimental results include sampling/detection rates and position estimation errors as functions of illuminance and motion speed; and the minimum pixel latency of two commercial state-of-the-art event-cameras (ATIS, DVS240). Event-cameras respond more slowly to positive than to negative large and sudden contrast changes. They outperformed a frame-camera in bandwidth efficiency in all our experiments. Both camera types provide comparable position estimation accuracy. The better event-camera was limited by pixel latency when tracking small objects, resulting in motion blur effects. Sensor bandwidth limited the event-camera in object recognition. However, future generations of event-cameras might alleviate bandwidth limitations.

An Extended Modular Processing Pipeline for Event-Based Vision in Automatic Visual Inspection

“…In previous works, DVS are mainly investigated in the field of autonomous driving, for monitoring, and gesture recognition. Additionally, it has recently been shown that the strength of the concept is particularly evident in sparsely populated scenes [2]. However, this does match very well to the fields of application considered so far.…”

Artificial intelligence-based spatio-temporal vision sensors: applications and prospects

“…Two different reference voltages are set, by integrating the light intensity, and recording the events that reach the two voltages. Because the time required for the voltage to change by the same amount is different under the conditions of different light intensities, by establishing the light intensity and time mapping can infer that the light intensity is small, so as to output the light intensity information at the pixel where the light intensity changes, which is also called Pulse Width Modulation (PWM) (Holesovsky et al, 2021). In addition, in order to solve the problem that the visual texture information of the static area cannot be obtained without DVS pulse signal issuance, ATIS introduces a set of global emission mechanism, that is, all pixels can be forced to emit a pulse, so that a whole image is used as the background, and then the moving area continuously generates pulses and then continuously triggers the light intensity measurement circuit to obtain the grayscale of the moving area to update the background (Fu et al, 2023).…”

“…The commercial ATIS (Holesovsky et al, 2021) developed by the Posch team and Prophesee company has a spatial resolution of 304 × 240, a sampling frequency of 106 Hz in the time domain, and a dynamic range of 143 dB, which is widely used in high-speed vision tasks. In addition, Prophessee has also received a $15 million project from Intel Corporation to apply ATIS to the vision processing system of autonomous vehicles.…”

Sprite Durations Measured With a Neuromorphic Sensor

“…Each pixel of the sensor operates independently and only reports changes in brightness which surpass a preset threshold, greatly reducing the required data rate by eliminating redundant information. These systems also carry the benefit of a higher dynamic range and a higher maximum "equivalent frame rate" than cameras of comparable cost (Holesovsky et al, 2021;McReynolds et al, 2023;Scheerlinck, 2021). These proposed higher speeds and significantly lower data rates in principle have a lot of potential for sprite observations.…”