Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world

On-line control of active camera networks

Large networks of cameras have been increasingly employed to capture dynamic events for tasks such as surveillance and training. When using active (pan-tilt-zoom) cameras to capture events distributed throughout a large area, human control becomes impractical and unreliable. This has led to the development of automated approaches for on-line camera control. I introduce a new approach that consists of a stochastic performance metric and a constrained optimization method. The metric quantifies the uncertainty in the state of multiple points on each target. It uses state-space methods with stochastic models of the target dynamics and camera measurements. It can account for static and dynamic occlusions, accommodate requirements specific to the algorithm used to process the images, and incorporate other factors that can affect its results. The optimization explores the space of camera configurations over time under constraints associated with the cameras, the predicted target trajectories, and the image processing algorithm. While an exhaustive exploration of this parameter space is intractable, through careful complexity analysis and application domain observations I have identified appropriate alternatives for reducing the space. Specifically, I reduce the spatial dimension of the search by dividing the optimization problem into subproblems, and then optimizing each subproblem independently. I reduce the temporal dimension of the search by using empirically-based heuristics inside each subproblem. The result is a tractable optimization that explores an appropriate subspace of the parameters, while attempting to minimize the risk of excluding the global optimum. The approach can be applied to conventional surveillance tasks (e.g., tracking or face recognition), as well as tasks employing more complex computer vision methods (e.g., markerless motion capture or 3D reconstruction). I present the results of experimental simulations of two such scenarios, using controlled and natural (unconstrained) target motions, employing simulated and real target tracks, in realistic scenes, and with realistic camera networks

Cognitive visual tracking and camera control

Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision