Classical particle tracking using frame-based Active Pixel Sensor (APS) struggles with poor lighting conditions, scenes with high dynamic range, dynamic changes in illuminance and restricted frame rate. We extend Particle Tracking Velocimetry (PTV) to such difficult environmental conditions by utilizing Event-Based Vision (EBV) sensors, which asynchronously generate timestamped events in response to pixel intensity changes.

Point clouds are a promising video representation for virtual and augmented reality. Their high-bitrate, however, has so far limited the practicality of live streaming systems. In this work, we leverage the delivery timeout feature within the Media Over QUIC protocol to perform implicit server-side adaptation based on an application's latency target. Through experimentation with several publisher and network configurations, we demonstrate that our system unlocks a unique trade-off on a per-client basis: applications with lower latency requirements will receive lower-quality video, while applications with more relaxed latency requirements will receive higher-quality video.

Automatic image cropping is a method for maximizing the human-perceived quality of cropped regions in photographs. Although several works have proposed techniques for producing singular crops, little work has addressed the problem of producing multiple, distinct crops with aesthetic appeal. In this paper, we motivate the problem with a discussion on modern social media applications, introduce a dataset of 277 relevant images and human labels, and evaluate the efficacy of several single-crop models with an image partitioning algorithm as a pre-processing step. The dataset is available at https://github.com/RafeLoya/carousel.

While traditional video representations are organized around discrete image frames, event-based video is a new paradigm that forgoes image frames altogether. Rather, pixel samples are temporally asynchronous and independent of one another. Until now, researchers have lacked a cohesive software framework for exploring the representation, compression, and applications of event-based video. Rather, they have focused on applications for individual cameras and data types. The ADΔER software suite aims to fill this gap. This framework includes utilities for transcoding framed and multimodal event-based video sources to a common representation, rate control mechanisms, lossy compression, application support, and an interactive GUI for transcoding and playback. This paper describes these various components and makes the open-source software available at https://github.com/ac-freeman/adder-codec-rs.

The strong temporal consistency of surveillance video enables compelling compression performance with traditional methods, but downstream vision applications operate on decoded image frames with a high data rate. Since it is not straightforward for applications to extract information on temporal redundancy from the compressed video representations, we propose a novel system which conveys temporal redundancy within a sparse decompressed representation. We leverage a video representation framework called ADΔER to transcode framed videos to sparse, asynchronous intensity samples. We introduce mechanisms for content adaptation, lossy compression, and asynchronous forms of classical vision algorithms. We evaluate our system on the VIRAT surveillance video dataset, and we show a median 43.7% speed improvement in FAST feature detection compared to OpenCV. We run the same algorithm as OpenCV, but only process pixels that receive new asynchronous events, rather than process every pixel in an image frame. Our work paves the way for upcoming neuromorphic sensors and is amenable to future applications with spiking neural networks.

The concept of "video" is synonymous with frame-sequence image representations. However, neuromorphic "event" cameras, which are rapidly gaining adoption for computer vision tasks, record frameless video. We believe that these different paradigms of video capture can each benefit from the lessons of the other. To usher in the next era of video systems and accommodate new event camera designs, we argue that we will need an asynchronous, source-agnostic processing pipeline. In this paper, we propose an end-to-end framework for frameless video, and we describe its modularity and amenability to compression and both existing and future applications.

Neuromorphic "event" cameras, designed to mimic the human vision system with asynchronous sensing, unlock a new realm of high-speed and high-dynamic-range applications. However, researchers often either revert to a framed representation of event data for applications, or build bespoke applications for a particular camera's event data type. To usher in the next era of video systems, accommodate new event camera designs, and explore the benefits of asynchronous video in classical applications, we argue that there is a need for an asynchronous, source-agnostic video representation. In this paper, we introduce a novel, asynchronous intensity representation for both framed and non-framed data sources. We show that our representation can increase intensity precision and greatly reduce the number of samples per pixel compared to grid-based representations. With framed sources, we demonstrate that by permitting a small amount of loss through the temporal averaging of stable pixel values, we can reduce our representational sample rate by more than half, while incurring a drop in VMAF quality score of only 4.5. We also demonstrate lower latency than the state-of-the-art method for fusing and transcoding framed and event camera data to an intensity representation, while maintaining 2000X the temporal resolution. We argue that our method provides the computational efficiency and temporal granularity necessary to build real-time intensity-based applications for event video.

Event cameras are biologically-inspired sensors that upend the framed, synchronous nature of traditional cameras. Singh et al. proposed a novel sensor design wherein incident light values may be measured directly through continuous integration, with individual pixels' light sensitivity being adjustable in real time, allowing for extremely high frame rate and high dynamic range video capture. Up to this point, there has been little research into compressing this event data, and even less that has investigated a robust method of lossy compression. This paper makes an inroad in this area, proposing a lossy model-based compression scheme with user-controlled quality levels and examine its compression efficiency and reconstructed image quality for several synthetic scenes. In our experiments, we observe space savings over the original event stream upwards of 95% with only minor losses in reconstructed image quality.

Integrating event cameras are asynchronous sensors wherein incident light values may be measured directly through continuous integration, with individual pixels' light sensitivity being adjustable in real time, allowing for extremely high frame rate and high dynamic range video capture. This paper builds on lessons learned with previous attempts to compress event data and presents a new scheme for event compression that has many analogues to traditional framed video compression techniques. We show how traditional video can be transcoded to an event-based representation, and describe the direct encoding of motion data in our event-based representation. Finally, we present experimental results proving how our simple scheme already approaches the state-of-the-art compression performance for slow-motion object tracking. This system introduces an application "in the loop" framework, where the application dynamically informs the camera how sensitive each pixel should be, based on the efficacy of the most recent data received.

Event cameras are biologically-inspired sensors that upend the framed, synchronous nature of traditional cameras. Singh et al. proposed a novel sensor design wherein incident light values may be measured directly through continuous integration, with individual pixels' light sensitivity being adjustable in real time, allowing for extremely high frame rate and high dynamic range video capture. Arguing the potential usefulness of this sensor, this paper introduces a system for simulating the sensor's event outputs and pixel firing rate control from 3D-rendered input images.