Movement Detection with Event-Based Cameras: Comparison with Frame-Based Cameras in Robot Object Tracking Using Powerlink Communication

Abstract

Event-based cameras are not common in industrial applications despite the fact that they can add multiple advantages for applications with moving objects. In comparison with frame-based cameras, the amount of generated data is very low while keeping the main information in the scene. For an industrial environment with interconnected systems, data reduction becomes very important to avoid network congestion and provide faster response time. However, the use of new sensors as event-based cameras is not common since they do not usually provide connectivity to industrial buses. This work develops a network node based on a Field Programmable Gate Array (FPGA), including data acquisition and tracking position for an event-based camera. It also includes spurious reduction and filtering algorithms while keeping the main features at the scene. The FPGA node also includes the stack of the network protocol to provide standard communication among other nodes. The powerlink IEEE 61158 industrial network is used to communicate the FPGA with a controller connected to a self-developed two-axis servo-controlled robot. The inverse kinematics model for the robot is included in the controller. To complete the system and provide a comparison, a traditional frame-based camera is also connected to the controller. Response time and robustness to lighting conditions are tested. Results show that, using the event-based camera, the robot can follow the object using fast image recognition achieving up to 85% percent data reduction providing an average of 99 ms faster position detection and less dispersion in position detection (4.96 mm vs. 17.74 mm in the Y-axis position, and 2.18 mm vs. 8.26 mm in the X-axis position) than the frame-based camera, showing that event-based cameras are more stable under light changes. Additionally, event-based cameras offer intrinsic advantages due to the low computational complexity required: small size, low power, reduced data and low cost. Thus, it is demonstrated how the development of new equipment and algorithms can be efficiently integrated into an industrial system, merging commercial industrial equipment with new devices.

Experimental Comparison between Event and Global Shutter Cameras

“…Several authors deal with the comparison of event- and frame-cameras. Barrios-Avilés et al [ 5 ] test the object detection latency of event and standard cameras. Their vision system detects a black circular dot rotating on a white disk and estimates the position of the dot for control purposes.…”

Classification of Facet Systems of Technical Vision

“…Для керування такими засобами зазвичай використовують системи технічного зору, які здатні швидко і точно оцінити розташуванн навколишніх об'єктів у просторі та задавати нову траєкторію руху для уникнення зіткнень з завадами на своєму шляху. Серед таких систем можна навести цифрові камери так камери реєстрації подій (event-based cameras) [3,4]. Сучасні цифрові камери складаються з об'єктиву, що формує зображення в площині матричного фотоприймального пристрою та під'єднаного до нього цифрового сигнального процесору, який оброблює отриманні зображення.…”

Event Camera Based Real-Time Detection and Tracking of Indoor Ground Robots

“…The study highlighted the problems faced by standard optical flow algorithms such as Lucas-Kanade and local plane fit due to the noise in the event data stream and motion discontinuities. Event-based cameras in industrial robotics have recently become an active field of research [66]. In [66], the authors compared the performance of a 2-axis servo-controlled robot based on the data acquisition and motion tracking from an event camera and a framebased camera.…”

“…Event-based cameras in industrial robotics have recently become an active field of research [66]. In [66], the authors compared the performance of a 2-axis servo-controlled robot based on the data acquisition and motion tracking from an event camera and a framebased camera. The results showed that the robotic arm using the event-based camera could follow the object using image recognition while achieving up to 85% percent data reduction and providing an average of 99ms faster position detection than the frame-based camera.…”